RAG for AI Agents - Retrieval-Augmented Generation Explained

What is RAG?

Retrieval-Augmented Generation (RAG) is a technique that enhances LLM responses by retrieving relevant information from external sources before generating a response. Instead of relying solely on training data, RAG grounds responses in retrieved context.

How RAG Works

The RAG pipeline:

1. **Query**: User asks a question

2. **Retrieve**: Find relevant documents/memories

3. **Augment**: Add retrieved context to the prompt

4. **Generate**: LLM produces grounded response

RAG vs Fine-tuning

| Aspect | RAG | Fine-tuning |

|--------|-----|-------------|

| Knowledge updates | Real-time | Requires retraining |

| Cost | Lower (no training) | Higher (compute) |

| Accuracy | Good with good retrieval | Can be very high |

| Customization | Flexible | Fixed after training |

RAG for Agent Memory

RAG enables memory-augmented agents by:

Retrieving relevant past conversations

Grounding responses in user history

Accessing up-to-date information

Reducing hallucinations

Enabling personalization

RAG Components

Key components of a RAG system:

**Embedding Model**: Converts text to vectors

**Vector Store**: Indexes and searches embeddings

**Retriever**: Finds relevant documents

**Reranker**: Improves retrieval quality

**Generator**: Produces final response

Retrieval Strategies

Common retrieval approaches:

**Dense Retrieval**: Embedding-based similarity

**Sparse Retrieval**: BM25, keyword matching

**Hybrid**: Combining dense and sparse

**Multi-query**: Generate multiple search queries

Advanced RAG Patterns

Sophisticated RAG techniques:

**Self-RAG**: Model decides when to retrieve

**Corrective RAG**: Validates and corrects retrieval

**Iterative RAG**: Multiple retrieval rounds

**Agentic RAG**: Agents controlling retrieval

Challenges

Common RAG challenges:

Retrieval quality ("garbage in, garbage out")

Context window limits

Latency from retrieval step

Chunking strategies

Handling no relevant results

RAG (Retrieval-Augmented Generation)