Retrieval-Augmented Generation (RAG)

Traditional vs. Agentic RAG, clearly explained!

The future is Agentic RAG, and it's because a traditional RAG setup has some major limitations...👇

1) Retrieve once and generate once.
↳ This means if the retrieved context isn't enough or correct, the LLM can not dynamically search for more information.

2) Inability to reason through complex queries.
↳ If a query requires multiple retrieval steps or CoT (chain of thought), traditional RAG falls short.

3) Limited adaptability
↳ The system can't modify its strategy based on the problem at hand. Eg. Whether to do vector search, web search or call an API.

Agentic RAG addresses these issues.

The core idea is to introduce agentic behaviors at each stage of RAG.

Agents can actively think through tasks—planning, adapting, and iterating to find the best solution, rather than just following a set of instructions, and LLMs enable this.

The image below illustrates the workflow of an agentic RAG. Refer to it as you continue reading...

Steps 1-2) The user inputs a query, and an agent refines it (corrects spelling, simplifies for embedding, etc.)

Step 3) Another agent decides if more details are needed.
↳ Step 4) If not, the refined query is sent to the LLM.
↳ Steps 5-8) If yes, the agent selects the relevant sources (vector database, tools/APIs, internet), retrieves context, and sends it to the LLM.

Step 9) A response is generated.

Step 10) A final agent checks if the answer is relevant.
↳ Step 11) If yes, return the response.
↳ Step 12) If no, restart from Step 1. This process repeats until the system provides an acceptable answer or concedes it cannot respond.

This makes the RAG much more dynamic and robust.

However, it's important to note that building RAG systems often comes down to design preferences and choices.

The diagram below is just one of many blueprints an agentic RAG system may have.

You can adapt it to suit your specific use case.

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Hands-On AI_ RAG using LlamaIndex Part 01 to Part 04

Part 01: https://t.me/AIMLDeepThaught/959
Part 02: https://t.me/AIMLDeepThaught/967
Part 03: https://t.me/AIMLDeepThaught/975
Part 04: https://t.me/AIMLDeepThaught/979

1. Introduction

01. Overcome the limitations of LLMs with RAG
02. Limitations of LLMs
03. Use cases for retrieval-augmented generation RAG

2. Getting Started

01. Using GitHub Codespaces
02. Setting up your environment
03. Choosing an LLM and embeddings provider
04. Setting up LLM accounts
05. Choosing a vector database
06. Setting up a Qdrant account
07. Downloading our data

3. Fundamental Concepts in LlamaIndex

01. How LlamaIndex is organized
02. Using LLMs
03. Loading data
04. Indexing
05. Storing and retrieving
06. Querying
07. Agents

Hands-On AI_ RAG using LlamaIndex Part 02

4. Introduction to RAG

01. Components of a RAG system
02. Ingestion pipeline
03. Query pipeline
04. Prompt engineering for RAG
05. Data preparation for RAG
06. Putting it all together
07. Drawbacks of Naive RAG

5. RAG Evaluation

01. Introduction to RAG evaluation
02. Evaluation metrics
03. How to create an evaluation set

Hands-On AI_ RAG using LlamaIndex - Part 03

5. Advanced RAG Pre-Retrieval and Indexing Techniques

01. How we can improve on Naive RAG
02. Optimizing chunk size
03. Small to big retrieval
04. Semantic chunking
05. Metadata extraction
06. Document summary index
07. Query transformation

6. Advanced RAG Post-Retrieval and Other Techniques

01. Node post-processing
02. Re-ranking
03. FLARE
04. Prompt compression
05. Self-correcting

Hands-On AI_ RAG using LlamaIndex - Part 04

7. Modular RAG

01. Hybrid retrieval
02. Agentic RAG
03. Ensemble retrieval
04. Ensemble query engine

8. Conclusion

01. LlamaIndex evaluation
02. Comparative analysis of retrieval-augmented generation technique

𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘃𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀

When should you choose one over the other

Both models are popular for binary classification tasks, but their assumptions and behavior are very different. Choosing the right one depends on data complexity, interpretability needs, and the structure of the input features.

Here are some practical guidelines:

- Use logistic regression when your features have a linear relationship with the target and you want interpretability

- Use decision trees when the data contains complex patterns or nonlinear boundaries

- Logistic regression is sensitive to outliers and multicollinearity, trees handle those better

- Decision trees can easily overfit if not pruned or regularized, while logistic regression tends to generalize more smoothly

- Logistic regression gives probabilistic outputs by nature, which can be useful for threshold-based decisions

In short, logistic regression works best when the relationships are relatively simple and you want transparency. Decision trees shine when you need flexibility or when feature interactions are important.

Logistic regression works well for problems like spam detection, churn prediction, or medical diagnosis, where relationships between variables are relatively simple and explainability matters

Decision trees are better suited for tasks like credit risk scoring, fraud detection, or customer segmentation, where nonlinear patterns and rule based splits improve accuracy

Choosing the right model for the data and context leads to more robust and interpretable results

𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘃𝘀 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀

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🔰 AI Agents: Building Teams of LLM Agents that Work For You

1. Defining LLMs

2. Building an Autocomplete

3. From Autocomplete to LLMs

4. Prompt Engineering

5. Agentic Design