What does it take to become a specialist in a particular skill? It is said, the learner should invest around 10,000 hours of focused practice to gain expertise in a field. But in this fast-paced world, where time is the most valuable thing, we need to work smarter to plan how a beginner can get a strong hold on a tech-specific skill in a limited time. The answer lies in having a clear Learning Path or a perfect Roadmap. It Worked for Me! Today, I am going to talk about how you can become a RAG Specialist, and I will provide a detailed roadmap for diving into the world of Retrieval Augmented Generation (RAG).
RAG Specialist Roadmap is for:
- Python developers & ML Engineers who want to build AI-driven applications leveraging LLMs and custom enterprise data.
- Students and Learners willing to dive into RAG implementations and gain hands-on experience with practical examples.
Table of contents
- What is RAG, and Where is it Used?
- How RAG Works?
- Learning Path to Become a RAG Specialist
- Step 1. Programming Language Proficiency
- Step 2. Core Libraries and Tools
- Step 3. Foundations of Machine Learning and Deep Learning – with a Focus on Information Retrieval
- Step 4. Natural Language Processing (NLP)
- Step 5. Introduction to RAG Systems
- Step 6. Retrieval-Augmented Generation (RAG) Architecture
- Step 7. Information Retrieval (IR)
- Step 8. Building Retrieval Systems
- Step 9. Integration into RAG Systems
- Step 10. RAG Evaluation
- Step 11. RAG Challenges and Improvements
- Step 12. Practical Implementation
- Step 13. Advanced RAG
- Step 14. Ongoing Learning and Resources
- Step 15. Community and Continuous Learning
- Step 16. Hands-On Capstone Project
- RAG Research Papers
- Conclusion
- Frequently Asked Questions
Click here to download the RAG Specialist roadmap!
What is RAG, and Where is it Used?
RAG (Retrieval-Augmented Generation) is a technique that enhances the performance of language models by combining them with an external retrieval mechanism. This allows the model to pull in relevant information from large document stores or knowledge bases at inference time, improving the quality and factual accuracy of its generated responses.
Key Components of RAG:
- Retrieval Component: A retriever (typically based on similarity search) scans a large corpus of documents or databases to find relevant passages based on a query.
- Generation Component:
- After retrieving the relevant documents or passages, a language model (e.g., GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.2) uses these passages as context to generate a more informed response or output.
- The model can either generate a direct answer or summarize the retrieved information depending on the task.
The main advantage of RAG is that it allows the model to handle long-tail knowledge and tasks that require factual accuracy or specialized knowledge, which might not be directly encoded in the model’s parameters.
Also read: Top 8 Applications of RAGs in Workplaces.
How RAG Works?
Here’s how RAG works:
- When a query or prompt is received, the system first retrieves relevant documents or information from a pre-indexed corpus (such as Wikipedia, product catalogs, research papers, etc.).
- The language model then uses the retrieved information to generate a response.
- The model might perform multiple retrieval steps (iterative retrieval) or use a combination of different retrieval techniques to improve the quality of the retrieved documents.
To know more about this, refer to this article: What is Retrieval-Augmented Generation (RAG)? Build a RAG Pipeline With the LLama Index.
Learning Path to Become a RAG Specialist
To become an RAG specialist, you’ll need to gain expertise in multiple areas, ranging from foundational knowledge in machine learning and natural language processing (NLP) to hands-on experience with RAG-specific architectures and tools. Below is a comprehensive learning path tailored to guide you through this journey to becoming an RAG Specialist:
Step 1. Programming Language Proficiency
Master the primary programming languages used in Retrieval-Augmented Generation (RAG) development, with a strong focus on Python.
Languages:
- Python: The dominant language in AI/ML research and development. Python is widely used for data science, machine learning, natural language processing (NLP), and creating systems that rely on RAG methods. Its simplicity, combined with an extensive ecosystem of libraries, makes it the go-to choice for AI and ML tasks.
Key Skills:
- Data structures (lists, dictionaries, sets, tuples).
- File handling (text, JSON, CSV).
- Exception handling and debugging.
- Object-oriented programming (OOP) and functional programming concepts.
- Writing modular and reusable code.
Resources:
- “Automate the Boring Stuff with Python” by Al Sweigart – A great resource for beginners that covers Python basics with real-world applications, focusing on practical scripting for automation and productivity.
- “Python Crash Course” by Eric Matthes – A beginner-friendly book that offers a comprehensive introduction to Python, covering all essential topics and providing hands-on projects to build your skills.
For more books:
Step 2. Core Libraries and Tools
Gain familiarity with the libraries and tools crucial for building and deploying Retrieval-Augmented Generation (RAG) systems. These libraries help streamline the process of data processing, data retrieval, model development, natural language processing (NLP), and integration with large-scale systems.
Key Libraries
- Machine Learning & Deep Learning:
- TensorFlow, PyTorch (for training and deploying models).
- Scikit-learn (for preprocessing and auxiliary tasks).
- NLP-Specific:
- Hugging Face Transformers (pretrained models like GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.2).
- SpaCy and NLTK (text preprocessing and linguistic features).
- Data Processing:
- Pandas (data manipulation).
- NumPy (numerical computing).
- PyTorch Lightning (scalable ML workflows).
- PyTorch Litserve
Resources
- Official documentation for TensorFlow, PyTorch, Hugging Face, SpaCy, and other libraries.
- GitHub repositories for RAG-specific frameworks (e.g., Haystack, PyTorch Lightning, listserve, LangChain and LlamaIndex).
- Online tutorials and courses (e.g., Analytics Vidhya, Deeplearning.ai, Coursera, edX, Fast.ai) covering deep learning, NLP, and RAG development.
- Course on Python: Introduction to Python
Also explore: Coding Essentials Course
Step 3. Foundations of Machine Learning and Deep Learning – with a Focus on Information Retrieval
The foundations of Machine Learning and Deep Learning in RAG (Retriever-Augmented Generation) is to equip learners with the essential knowledge of machine learning and deep learning techniques. This involves understanding model architectures, data retrieval methods, and the integration of generative models with information retrieval systems to enhance the accuracy and efficiency of AI-driven responses and tasks.
Key Topics:
- Supervised Learning: Learning from labeled data to predict outcomes (e.g., regression and classification).
- Unsupervised Learning: Identifying patterns and structures in unlabeled data (e.g., clustering and dimensionality reduction).
- Reinforcement Learning: Learning by interacting with an environment and receiving feedback through rewards or penalties.
- Core Algorithms:
- Linear Regression: For predicting continuous outcomes.
- Logistic Regression: For binary classification.
- Decision Trees: For decision-making based on data features.
- K-Means Clustering: For grouping similar data points.
- K-Nearest Neighbors (KNN): For classification based on proximity to labeled data points.
- Information Retrieval (IR) Systems: Information Retrieval refers to the process of obtaining relevant information from large datasets or databases, typically in response to a query. The core components include:
- Search Engine Basics:
- Indexing: Involves creating an index of all documents in a corpus to facilitate fast retrieval based on the search terms.
- Query Processing: When a user enters a query, the system processes it, matches it to relevant documents in the index, and ranks the documents based on relevance.
- Ranking Algorithms: Ranking is typically based on algorithms like TF-IDF (Term Frequency-Inverse Document Frequency), which measures the importance of a term in a document relative to its occurrence in the entire corpus.
- Search Engine Basics:
- Vector Space Model (VSM): Documents and queries are represented as vectors in a multi-dimensional space, where each dimension represents a term. The similarity between a query and a document is determined using measures like Cosine Similarity.
- Latent Semantic Analysis (LSA): A technique used to reduce dimensionality and capture deeper semantic relationships between terms and documents through Singular Value Decomposition (SVD).
- BM25, Cosine Similarity and PageRank for ranking document relevance.
- Clustering: Clustering is a type of unsupervised learning where data points are grouped into clusters based on similarity, without prior labels.
- K-Means Clustering: A widely used algorithm that divides data into k clusters by minimizing the variance within each cluster.
- Hierarchical Clustering: Builds a tree-like structure of nested clusters, where each level represents a different level of granularity.
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise): A density-based clustering algorithm that can find clusters of arbitrary shape and is good at identifying noise (outliers).
- Clustering Evaluation:
- Silhouette Score: Measures how similar an object is to its own cluster compared to other clusters.
- Dunn Index: Measures the ratio of the minimum inter-cluster distance to the maximum intra-cluster distance.
- Vector Similarity
- Cosine Similarity: Measures the cosine of the angle between two vectors. It’s commonly used in IR to measure document-query similarity.
- Euclidean Distance: The straight-line distance between two vectors. Less commonly used in IR compared to cosine similarity, but often applied in clustering.
- Word Embeddings (Word2Vec, GloVe, FastText): Word embeddings map words to dense vectors that capture semantic meanings, making them highly effective in measuring similarity between words or phrases.
- Recommendation Systems: Recommendation systems aim to predict the most relevant items for users based on their behavior, preferences, or the behavior of similar users. There are generally two main types of recommender systems
- Collaborative Filtering:
- User-based Collaborative Filtering: Recommends items by finding similar users and suggesting what they liked.
- Item-based Collaborative Filtering: Recommends items that are similar to those the user has already liked.
- Matrix Factorization: Decomposes the user-item interaction matrix into two lower-dimensional matrices representing users and items, respectively. Techniques like SVD (Singular Value Decomposition) and ALS (Alternating Least Squares) are commonly used.
- Content-Based Filtering:
- Recommends items based on the features of items the user has liked. For example, if a user liked action movies, the system may recommend other action movies based on metadata (e.g., genre, actors).
- Hybrid Methods:
- Combine both collaborative and content-based approaches to enhance recommendations by leveraging both user behavior and item features.
- Evaluating Recommender Systems:
- Precision/Recall: Measures the relevance of recommendations.
- Mean Absolute Error (MAE): Measures the accuracy of predicted ratings.
- Root Mean Squared Error (RMSE): Another measure of prediction accuracy, with a stronger penalty for large errors.
- Collaborative Filtering:
Practical Techniques & Models in Information Retrieval
- TF-IDF (Term Frequency-Inverse Document Frequency):
- Measures the importance of a word in a document relative to the entire corpus. Frequently used in text-based information retrieval.
- BM25 (Best Matching 25):
- An extension of TF-IDF, this probabilistic ranking function accounts for term frequency saturation and document length, often used in modern search engines like Elasticsearch.
- Latent Dirichlet Allocation (LDA):
- A generative probabilistic model used for topic modeling, which finds topics in a collection of documents based on word distributions.
Resources:
- Books:
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani: A comprehensive guide to the theory and practical applications of machine learning.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron: A practical guide to implementing ML algorithms with Python.
Also read: Must-Read Books for Beginners on Machine Learning and Artificial Intelligence
Courses:
- Andrew Ng’s Machine Learning Course (Coursera): A well-structured, beginner-friendly course that covers core concepts and algorithms in ML with hands-on programming assignments.
- Course: Machine Learning Certification Course for Beginners
Online Resources:
- TensorFlow Documentation: Official documentation for TensorFlow, one of the most popular deep learning frameworks, offering tutorials and guides.
- PyTorch Documentation: Comprehensive resources for learning PyTorch, another leading deep learning framework known for its flexibility and ease of use.
Here are more books to read:
- SuperIntelligence
- The Master Algorithm
- Life 3.0
- AI Superpowers
- Moneyball
- Scoring Points
- The Singularity is Near
Step 4. Natural Language Processing (NLP)
To truly understand how Retrieval-Augmented Generation (RAG) systems work, it’s crucial to delve into the foundational NLP techniques. These form the core of processing, representing, and understanding text data in a computational framework. Below is a breakdown of the essential concepts related to text preprocessing, word embeddings, and language models, along with their applications in various NLP tasks like classification, search, similarity, and recommendations.
Key Topics:
- Using NLTK for Text Processing: tokenization, stemming, lemmatization.
- Tokenization: Split text into words or sentences.
- Example: nltk.word_tokenize(“I love pizza!”) → [‘I’, ‘love’, ‘pizza’, ‘!’]
- Stemming: Reduces words to their root form.
- Example: nltk.PorterStemmer().stem(“running”) → “run”
- Lemmatization: Converts words to their base form, considering context.
- Example: nltk.WordNetLemmatizer().lemmatize(“better”, pos=”a”) → “good”
- Stopword Removal: Common words (e.g., “the”, “is”) can be removed to focus on meaningful terms.
- Example: nltk.corpus.stopwords.words(‘english’) provides a list of common stopwords.
- Tokenization: Split text into words or sentences.
- Word embeddings: Word2Vec, GloVe, fastText.
- Large Language models: GPT-4o, Claude 3.5, Gemini 1.5 and open-source (Llama 3.2, Mistral) through platforms like Hugging Face and Groq.
- Sequence-to-sequence models and attention mechanisms: Sequence-to-sequence (Seq2Seq) models are designed to map one sequence of tokens to another. This architecture is fundamental for tasks like translation, summarization, and dialog systems.
- Text Classification: NLP models classify text into predefined categories. For instance, sentiment analysis (positive/negative) is a typical text classification task. Word embeddings and transformers are used to classify text into different categories, making them effective for tasks like spam detection or sentiment analysis.
- Search and Information Retrieval: By converting words into embeddings, NLP systems can evaluate the semantic similarity between different pieces of text. This is crucial for building systems that can retrieve relevant documents or answers based on a query. For example, RAG systems use retrieval techniques to augment generative models with external knowledge from documents.
- Similarity and Recommendations: Word embeddings can be used to measure the semantic similarity between text or items. For example, in recommender systems, text embeddings can help recommend items that are semantically similar to a user’s query or past behavior. Similarly, similarity measures (e.g., cosine similarity) between vector embeddings are widely used in tasks like document retrieval and paraphrase detection.
Numeric Vectors: Sparse vs. Dense Embeddings
- Sparse Vectors: High-dimensional vectors where most values are zero. Used in traditional models like BoW or TF-IDF, they capture word frequency but miss semantic relationships.
- Example: “I love pizza” → [1, 1, 1, 0, 0] (based on a fixed vocabulary)
- Dense Embeddings: Continuous, low-dimensional vectors that capture semantic meaning. Generated by models like Word2Vec, GloVe, or BERT.
- Example: “King” and “Queen” have similar dense vector representations, capturing their semantic relationship.
- Resources:
- Books:
- “Speech and Language Processing” by Daniel Jurafsky and James H. Martin – A comprehensive textbook that covers a wide range of NLP topics, from text preprocessing and word embeddings to deep learning models like transformers.
- “Natural Language Processing with Python” by Steven Bird, Ewan Klein, and Edward Loper – A practical guide for applying NLP techniques using Python, including tools like NLTK and other useful libraries for text processing.
- Courses
- Introduction to Natural Language Processing – Natural Language Processing (NLP) is the art of extracting information from unstructured text. This course teaches you basics of NLP, Regular Expressions and Text Preprocessing.
- Books:
Link: Introduction to Natural Language Processing
- Natural Language Processing with Python (Udemy, edX) – A hands-on course that covers core NLP concepts, from basic text processing to advanced models like transformers. This course often includes practical examples and projects to deepen your understanding.
- Stanford NLP Course (CS224n) – A more advanced course focused on deep learning for NLP, covering transformer models, attention mechanisms, and practical implementations.
- Deep Learning for NLP (Coursera, Andrew Ng) – A specialized course focusing on using deep learning techniques for NLP, including sequence-to-sequence models and transformers.
Tools: Tools like NLTK and SpaCy are essential for building NLP pipelines.
Courses:
- Introduction to Natural Language Processing
- Building Production-Ready RAG systems using LlamaIndex Building Your first RAG System using
- LlamaIndex – Free Course Improving Real-World RAG Systems: Key Challenges & Practical Solutions
Prompt Engineering
It’s also essential to understand how to access and prompt both open-source and commercial models. For example, open-source models like Llama 3.2, Gemma 2, and Mistral can be accessed through platforms like Hugging Face or Groq. These platforms offer APIs that simplify the integration of these models into applications. Similarly, for commercial models like GPT-4, Gemini 1.5, and Claude 3.5, knowing how to properly prompt these systems is crucial to getting optimal results.
In addition, an understanding of prompt engineering—the practice of crafting precise and effective prompts—is indispensable. Whether you’re working with open-source or commercial models, knowing how to guide the model’s responses is a skill that greatly impacts the performance of RAG systems. Learning the essentials of prompt engineering will help you build more efficient and scalable NLP applications.
Also read: Prompt Engineering Roadmap
Step 5. Introduction to RAG Systems
Understand the fundamentals of Retrieval-Augmented Generation (RAG) systems, a powerful approach that combines retrieval-based information retrieval (IR) and natural language generation (NLG) to tackle knowledge-intensive NLP tasks.
Key Topics:
- Overview of RAG architecture: The architecture of an RAG system is designed to combine the strengths of information retrieval (IR) with generative models. A typical RAG setup includes two primary components: the retriever and the generator.
- Difference between traditional IR and RAG.
- Prompt Engineering vs. RAG vs. Fine-Tuning
- RAG vs. Agents vs. Agentic RAG
Use cases:
- Knowledge-Intensive Tasks: RAG is well-suited for tasks that require detailed knowledge or facts beyond what is available in the model’s pre-trained weights. For example, in legal, scientific, or historical domains, RAG systems can fetch the latest research, case law, or historical documents and generate contextually informed answers or summaries.
- Question Answering (QA): RAG systems excel at open-domain question answering, where the query may cover a vast amount of potential topics. The retrieval step helps ensure that the answer is informed by relevant and up-to-date information.
- Summarization: RAG can be used for extractive or abstractive summarization by first retrieving relevant content (e.g., documents, articles, reports) and then generating a concise and coherent summary.
- Text Generation: For tasks requiring coherent and informed text generation, such as writing assistants or creative content generation, RAG can pull in real-world context from the retrieval step to ensure that generated text is not only fluent but also informed by accurate, up-to-date information.
Resources
- “RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (Lewis et al., 2020) – This foundational paper introduces the RAG framework and discusses its application to question answering and other knowledge-intensive tasks.
- “Dense Passage Retrieval for Open-Domain Question Answering” (Karpukhin et al., 2020)
- Tutorials on Hugging Face and OpenAI.
Course: RAG System Essentials
Resources from Analytics Vidhya
- A Comprehensive Guide to Building Agentic RAG Systems with LangGraph
- Graph RAG: Enhancing Retrieval-Augmented Generation with Graph Structures
- A Guide to Evaluate RAG Pipelines with LlamaIndex and TRULens
- Enhancing RAG with Retrieval Augmented Fine-tuning
- Unveiling Retrieval Augmented Generation (RAG)| Where AI Meets Human Knowledge
- Understanding Multimodal RAG: Benefits and Implementation Strategies
- Improving Real-World RAG Systems: Key Challenges & Practical Solutions
Step 6. Retrieval-Augmented Generation (RAG) Architecture
Understand the architecture and workflow of RAG systems, which combine information retrieval (IR) and natural language generation (NLG) to enhance the capabilities of NLP tasks, especially those involving large-scale knowledge or external sources.
Key Topics:
- Introduction to RAG: RAG systems combine information retrieval (IR) with natural language generation (NLG) to generate more informed and contextually relevant outputs. The retrieval step pulls in relevant documents or knowledge from an external corpus or database, which the generation module then uses to craft accurate and fluent responses. This allows RAG systems to answer questions, summarise information, and generate text based on real-world, up-to-date knowledge.
- Chunking: Chunking refers to the process of breaking text into smaller, more manageable pieces or “chunks” (e.g., sentences, paragraphs, or fixed-length spans of text). This is a critical step in both document indexing and retrieval.
- Text Chunking
- Semantic Chunking
- Vector Embeddings: Vector embeddings represent text in a continuous vector space, capturing semantic meaning. These embeddings enable efficient information retrieval by representing each document and query as a high-dimensional vector, where the distance between vectors corresponds to semantic similarity.
- Vector Database: The vector database stores and manages vectorized representations of documents or passages. The database facilitates fast retrieval by indexing vectors and allowing similarity searches based on vector proximity.
- Two-stage architecture: RAG systems typically have a two-stage architecture: Retriever + Generator.
- Dense passage retrieval (DPR): Dense Passage Retrieval (DPR) is a technique for efficiently retrieving passages from a large corpus, using dense vector embeddings for both the query and the passage. It contrasts with traditional keyword-based retrieval, which can be less flexible and less effective when the query and document use different vocabularies.
- Training retriever and generator modules: Training a RAG system typically involves training two separate modules: Training the Retriever and Training the Generator.
Hands-on:
- Implement RAG using frameworks like LangChain and LlamaIndex.
Resources:
- Papers:
- “RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” by Lewis et al. (2020) – The foundational paper for understanding the RAG architecture. It introduces the retrieval-augmented generation framework, providing insights into the model’s design, training, and performance on QA tasks.
- “Dense Retriever for Open-Domain Question Answering” by Karpukhin et al. (2020) – Explains the Dense Passage Retrieval (DPR) technique used in RAG systems, detailing its architecture and performance compared to sparse retrieval methods.
- Tutorials
- Hugging Face RAG Tutorials: Hugging Face offers excellent tutorials demonstrating how to use the pre-trained RAG models for various NLP tasks, including question-answering, summarization, and more.
- PyTorch and Hugging Face Integration: Various community tutorials and blog posts on GitHub guide you through implementing RAG from scratch using PyTorch or Hugging Face’s transformer library.
Resources from Analytics Vidhya
- 15 Chunking Techniques to Build Exceptional RAG Systems
- How to Build a RAG Evaluator Python Package with Poetry?
- 12 RAG Pain Points and their Solutions
- How to Find the Best Multilingual Embedding Model for Your RAG?
- RAG Application with Cohere Command-R and Rerank – Part 1
Step 7. Information Retrieval (IR)
Master the principles of information retrieval, which is essential for the “retrieval” component of Retrieval-Augmented Generation (RAG). A RAG system’s efficient retrieval of relevant documents or information is crucial in generating accurate and contextually appropriate responses.
Key Topics:
- Indexing and searching: Indexing is the process of organizing and storing documents in a way that makes it efficient to retrieve relevant results in response to a query. Searching involves finding the best-matching documents based on a user’s query.
- Vector similarity measures (cosine similarity, Euclidean distance): In modern information retrieval, especially in systems like RAG, documents and queries are often represented as vectors in high-dimensional space. The degree of similarity between the query and a document is determined by how close their vectors are to each other.
- Dense retrieval methods (e.g., DPR, BM25): Dense retrieval refers to using dense vector representations (usually learned by deep neural networks) for retrieving relevant documents or information. This is in contrast to traditional sparse retrieval methods that rely on exact keyword matching.
- FAISS and approximate nearest neighbor (ANN) search: FAISS (Facebook AI Similarity Search) is a library designed for efficient similarity search, particularly in high-dimensional spaces. FAISS allows the implementation of approximate nearest neighbor (ANN) search, which is essential for real-time information retrieval in large-scale datasets.
Resources
- Books
- “Introduction to Information Retrieval” by Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze – A foundational text on information retrieval, covering both traditional and modern approaches to search, indexing, ranking, and retrieval models.
- “Search Engines: Information Retrieval in Practice” by Bruce Croft, Donald Metzler, and Trevor Strohman – A practical guide to building search engines and understanding the mathematical and algorithmic foundations of IR.
Step 8. Building Retrieval Systems
A. Loading Data
Learn to manage and preprocess data for retrieval: Upon receiving a user query, the vector database helps retrieve chunks relevant to the user’s request.
- Key Skills:
- Reading data from multiple formats (JSON, CSV, database, etc.).
- Cleaning, deduplication, and standardizing text data.
- Hands-On:
- Load a corpus (e.g., Wikipedia) and preprocess it for indexing.
- Tools:
- LangChain or LlamaIndex Data Loaders, PDF Loaders, Unstructured.io
B. Splitting and Chunking Data
Prepare data for retrieval and chunking to optimize retrieval and generation performance.
- Key Skills:
- Splitting long documents into retrievable chunks.
- Handling overlapping tokens for context preservation.
- Tokenization and sequence management for models like GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.2.
- Hands-On:
- Implement chunking with Hugging Face’s Tokenizer class.
- Libraries:
- LangChain or LlamaIndex Data Loaders, PDF Loaders, Unstructured.io.
C. Vector Databases and Retrievers
Build and query retrieval systems using vector embeddings.
- Key Topics:
- Dense vector embeddings vs. sparse retrieval techniques.
- Working with vector databases like FAISS, Pinecone, or Weaviate.
- Dense Passage Retrieval (DPR) setup and tuning.
- Hands-On:
- Index document embeddings using FAISS and query them efficiently.
- Experiment with hybrid retrieval (BM25 + dense vectors).
- Tools:
- FAISS, Elasticsearch, Pinecone.
To understand this better, check out this course: Introduction to Retrieval
Step 9. Integration into RAG Systems
Combine retrieval and generative capabilities in a seamless pipeline. Learn how to implement a Retrieval-Augmented Generation (RAG) system using popular frameworks like LangChain, Hugging Face, and OpenAI. This workflow enables the retrieval of relevant data and generation of responses using advanced NLP models.
Build Your Own RAG System:
- Utilize LangChain and OpenAI for quick implementation.
- Integrate retrieval and generation in a seamless pipeline.
Check out this article: What is Retrieval-Augmented Generation (RAG)?
Key Topics:
- Two-stage architecture: Retriever and Generator (Firstly: Load, Split, Embed, Store then, Retriever + Generator)
- The core of any RAG system is the two-stage architecture, where the task is split into two main phases:
- Retriever: Fetches relevant information from a large corpus based on the input query.
- Generator: Takes the retrieved information and generates coherent and contextually accurate outputs.
- Steps Involved: Firstly: Load, Split, Embed, Store, then Retriever + Generator.
- The core of any RAG system is the two-stage architecture, where the task is split into two main phases:
- JSON and PDF Loaders: Used to load the context. Recursive character text splitting, and chunking. Also, the OpenAI embedding model and LLM—GPT 4o-mini, Claude, and GPT 3.5 Turbo for all.
- Pre-trained language models (GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.2) for a generation: The generation stage of the RAG system often involves pre-trained language models. These models are fine-tuned for various text-generation tasks, such as question-answering, summarisation, or dialogue systems.
- RAG pipelines with Hugging Face: Hugging Face provides a robust Transformers library, which contains pre-trained models like GPT-4o, Claude 3.5, Gemini 1.5, and Llama 3.2, as well as tools for creating RAG pipelines. You can build and fine-tune a retrieval-augmented generation pipeline using Hugging Face’s easy-to-use APIs.
- Working with commercial and open-source models – Gpt-4o, Gemini 1.5, Claude 3.5 and Llama 3.2, Gemma 2, Mistral etc using Hugging Face or Groq, respectively.
Hands-On:
- Implement a pipeline where retrieved chunks feed into a generative model.
- Hands-On: Build a Simple RAG System
Frameworks:
- Hugging Face Transformers, LangChain, LlamaIndex, OpenAI, Groq
To understand this better, check out this course: RAG Systems Essentials
Step 10. RAG Evaluation
Master evaluation techniques and learn to tackle common challenges associated with RAG systems. Understanding how to evaluate the performance of RAG models is critical to refining and improving the system, while addressing typical challenges to ensure that the model operates effectively in real-world applications.
Key Topics:
- Evaluation Metrics: Evaluating RAG systems requires both intrinsic and extrinsic metrics to ensure the quality of the system’s outputs and its real-world applicability. These metrics assess both the effectiveness of the retrieval and the generation phases.
- Tools like RAGAS, DeepEval, LangSmith, Arize AI Phoenix, LlamaIndex are designed to help you monitor and refine your RAG pipeline.
- The Metrics include:
- Retriever Metrics: Contextual Precision, Contextual Recall, Contextual Relevancy
- Generator Metrics: Answer Relevancy, Faithfulness, Hallucination Check, LLM as a Judge (G-Eval)
- Common Pain Points and Solutions: Despite their effectiveness, RAG systems often face several challenges during deployment. Here, we’ll explore common issues and practical solutions.
- Address challenges such as hallucination, irrelevant retrievals, latency, and scalability.
- Explore real-world case studies for practical solutions.
Hands-On:
- Hands-On: Deep Dive into RAG Evaluation Metrics – Setup RAG System:
This focuses on setting up a Retrieval-Augmented Generation (RAG) system, including configuring the retriever and generator components for evaluation. - Hands-On: Deep Dive into RAG Evaluation Metrics – Retriever Metrics:
Here, the focus is on evaluating retriever performance, using metrics like recall, precision, and retrieval quality to assess how well the retriever fetches relevant documents. - Hands-On: Deep Dive into RAG Evaluation Metrics – Generator Metrics:
This examines generator metrics such as answer relevancy – LLM based, answer relevancy – Similarity based, faithfulness, hallucination check, G-Eval which assess the quality and relevance of the generated content in response to retrieved passages. - Hands-On: End-to-End RAG System Evaluation – Implementation:
In this part, you’ll implement a full RAG pipeline, combining both the retrieval and generation components, and evaluating the system’s end-to-end performance. - Hands-On: End-to-End RAG System Evaluation Concepts:
This introduces key concepts for evaluating an end-to-end RAG system, covering holistic metrics and practical considerations for performance assessment.
Resources from Analytics Vidhya
- Also read: Evaluating and Monitoring LLM & RAG Applications with Opik
- Also read: A Beginner’s Guide to Evaluating RAG Pipelines Using RAGAS
- Also read: A Guide to Evaluate RAG Pipelines with LlamaIndex and TRULens
Step 11. RAG Challenges and Improvements
To understand the challenges faced by Retrieval-Augmented Generation (RAG) systems and explore practical solutions and recent advancements that improve their performance. These improvements focus on optimizing retrieval, enhancing model efficiency, and ensuring more accurate and relevant outputs in AI applications.
Challenges:
- Missing Content: Retrieval-based systems sometimes fail to fetch relevant or complete information from the knowledge base or external sources, leading to incomplete or inaccurate responses.
- Top Ranked Documents: RAG systems can often retrieve documents that are not the most relevant to the query, either because of poor ranking models or insufficient context around the query.
- Not in Context: Retrieved documents or snippets may lack sufficient context to be useful for the model to generate meaningful, coherent, or relevant outputs.
- Not Extracted: Key information might not be extracted from the retrieved documents, even when those documents are relevant, due to limitations in extraction models or algorithms.
- Wrong Format: The output from RAG systems may not be in the correct or desired format, resulting in less useful or harder-to-process responses.
- Incorrect Specificity: Sometimes, the model may retrieve documents or generate responses that are too general or overly specific, leading to vague or irrelevant results.
- Incomplete Responses: Generated responses might lack depth or fail to fully address the user’s question due to insufficient or poorly structured retrieval.
Solutions:
- Use Better Chunking Strategies: Implementing more effective chunking strategies breaks documents into contextually meaningful segments, improving retrieval and relevance in tasks like question answering.
- Hyperparameter Tuning – Chunking & Retrieval: Fine-tuning hyperparameters for chunking and retrieval helps optimize the balance between retrieval quality and computational efficiency, enhancing overall performance.
- Use Better Embedder Models: Employing more powerful embedding models (e.g., using sentence transformers or domain-specific models) improves the quality and accuracy of semantic similarity matching during retrieval.
- Use Advanced Retrieval Strategies: Advanced strategies like hybrid retrieval (dense + sparse) or reranking improve the relevance and ranking of retrieved documents, boosting the final response quality.
- Use Context Compression Strategies: Context compression techniques, such as summarization or selective attention, reduce irrelevant information and improve the model’s ability to focus on essential content.
- Use Better Reranker Models: Leveraging advanced reranker models, such as those based on transformer architectures, refines the ranking of retrieved documents to maximize the relevance and quality of final responses.
Hands-on:
- Hands-on: Solution for Missing Content in RAG
- Hands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted _ Incorrect Specificity, Hands-on- Solution for Missed
Explore this Free Course to Know More: Improving Real World RAG Systems: Key Challenges & Practical Solutions
Resources from Analytics Vidhya
- Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization
- RAFT: Adapting Language Model to Domain Specific RAG
- End-to-End Training of Neural Retrievers for Open-Domain Question Answering
- RAGAS: Automated Evaluation of Retrieval Augmented Generation
- PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
- Retrieval-Augmented Generation for Large Language Models: A Survey
- HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
Step 12. Practical Implementation
Build real-world RAG systems:
Key Topics:
- Hands-on: Build a Simple RAG System: Learn how to construct a basic Retrieval-Augmented Generation (RAG) system that fetches relevant documents and uses them to enhance the generation of responses.
- Hands-on: Build a Contextual Retrieval Based RAG System: This step enhances the RAG system by incorporating context-aware retrieval, ensuring the documents retrieved are highly relevant to the specific query.
- Hands-on: Building a RAG System With Sources: Extend your RAG system by adding functionality to track and display the original sources of retrieved information, improving transparency and trustworthiness.
- Hands-on: Building a RAG System with Citations: Focus on constructing a RAG system that not only retrieves information but also generates proper citations for each source used in the response.
Also read: A Comprehensive Guide to Building Multimodal RAG Systems
Tools:
- JSON Loaders and PDF Loaders to load the text content.
- OpenAI Embedder to convert the text chunks into Embeddings vectors
- GPT-4o mini
- LangChain
- LangChain Chroma and Wrapper
To understand it better, check out this course: RAG System Essentials
Step 13. Advanced RAG
Dive into building an Advanced RAG System
Key Topics:
- Multi-user Conversational RAG System:
- What is Conversation?
- Need for Conversational Memory
- Conversational Chain with Memory in LCEL
- Multi-modal RAG (text, images, and audio): In a multi-modal RAG system, the retriever doesn’t just pull relevant text but also retrieves images, videos, or audio files that may help generate more informative or comprehensive answers. The generator then synthesizes information from these different modalities to create more nuanced responses.
- Agentic Corrective RAG: Agentic RAG (Corrective RAG – CRAG) refers to an enhanced version of the standard RAG system, incorporating corrective actions.
Also read: A Comprehensive Guide to Building Agentic RAG Systems with LangGraph
- Agentic RAG System: Agentic RAG introduces an agent that can autonomously query external sources, interact with APIs, or make decisions about what information to retrieve next. For example, in a medical application, an agent might dynamically search for the latest research papers, clinical trials, or consult a medical database during the conversation to provide up-to-date answers.
Also read: A Comprehensive Guide to Building Agentic RAG Systems with LangGraph - Self RAG: Self-RAG (Self-Reflective Retrieval-Augmented Generation) improves language model (LM) performance by allowing the model to adaptively retrieve relevant passages on demand and engage in self-reflection through reflection tokens. This dynamic retrieval and reflection process enhances factual accuracy and task-specific behavior, outperforming traditional retrieval-augmented models and large LLMs like ChatGPT in tasks such as open-domain QA and fact verification.
- Optimizations for Advanced RAG: While basic RAG systems have a two-stage architecture (retriever + generator), several advanced techniques and optimizations can improve their accuracy, speed, and scalability. These methods often focus on improving the retrieval phase, enhancing generation quality, or reducing computational overhead.
- Self-querying retrieval.
- Parent document retriever.
- Hybrid search (dense + sparse).
- Compressors and HyDE (Hypothetical Document Embedding).
- Query expansion and optimization
- Result re-ranking strategies
- Prompt caching implementation
- Performance optimization techniques
- Advanced indexing methods
Resources:
- Explore open-source projects on GitHub: Exploring open-source projects on GitHub provides hands-on examples of advanced RAG architectures and optimization techniques.
- RAGFlow by infiniflow
- Haystack by deepset-ai
- txtai by neuml
- STORM by stanford-oval
- LLM-App by pathwaycom
- FlashRAG by RUC-NLPIR
- Canopy by pinecone-io
- Hugging Face RAG: Hugging Face’s library provides pre-trained models, fine-tuning capabilities, and tutorials for working with RAG architectures.
- LangChain: LangChain is an open-source framework specifically designed for building RAG-based applications. It provides tools for chaining together language models, retrieval systems, and other components to create sophisticated NLP pipelines.
- IBM RAG Cookbook: A compendium of tips, tricks, and techniques for implementing and optimizing Retrieval Augmented Generation (RAG) solutions.
- IBM Watsonx.ai: The model can deploy RAG pattern to generate factually accurate output.
- Azure machine learning: Azure Machine Learning allows you to incorporate RAG in your AI using the Azure AI Studio or using code with Azure Machine Learning pipelines.
- Research papers and conference proceedings (e.g., ACL, NeurIPS, ICML).
- Follow state-of-the-art implementations on GitHub.
Hands-On:
- Also read: A Comprehensive Guide to Building Multimodal RAG Systems by Dipanjan Sarkar
- Also read: RAG vs Agentic RAG: A Comprehensive Guide.
Step 14. Ongoing Learning and Resources
Stay updated with the latest research and tools in RAG.
- Optional Reading Resources:
- Top 2024 RAG research papers and industry blogs.
- Follow the experts like Andrew Ng, Andrej Karpathy, Yann LeCun, and more.
- Practical Tools:
- Use LangChain for prototyping.
- CLIP for Multimodal Embedding, Multimodal LLM(GPT-4o, and others), Unstructured.io, OpenAI Embedders, LangChain Vectorstores Chroma, LangChain Text Splitters and more.
Step 15. Community and Continuous Learning
Stay updated and connected.
Activities:
- Analytics Vidhya blogs and courses
- Join ML/NLP communities (e.g., Hugging Face Forums, Reddit ML groups).
- Contribute to open-source RAG projects on GitHub.
- Attend workshops and conferences (e.g., NeurIPS, ACL, EMNLP).
Step 16. Hands-On Capstone Project
Build a fully functional RAG system to demonstrate expertise.
Project Ideas:
- The question-answering system using Wikipedia as the knowledge base.
- Custom domain chatbot leveraging RAG.
- Multimodal retrieval-augmented summarization tool.
By following this learning path, you can progress from foundational concepts to becoming an advanced RAG specialist. Regular hands-on practice, reading research papers, and engaging with the community will help solidify your expertise.
Moreover, here are the RAG research papers that you can explore to become an RAG specialist:
