RAG, chunking, embeddings: the practical guide for reliable chatbots (SEO, hands-on)

RAG, chunking, embeddings: the practical guide for reliable chatbots (SEO, hands-on)
Table of Contents

Why your RAG still hallucinates (and how to fix it)

Many believe that plugging a document base into an LLM guarantees factual answers. In reality, a poorly designed RAG pipeline (naive chunking, generic embeddings, noisy retrieval, missing citations) leads to hallucinations and production failures.

This guide gives you the keys to building a robust, indexable RAG pipeline. For more (scripts, evaluation, advanced cases), see The Mechanics of LLMs.

RAG: the winning recipe

A modern RAG pipeline:

  1. Chunk your documents intelligently (structure, overlap)
  2. Encode each chunk with domain-adapted embeddings (BGE, E5, OpenAI, Cohere…)
  3. Retrieve relevant passages (vector search, re-ranking)
  4. Augment the LLM prompt with these passages, enforcing citations and structure if needed

RAG is the “open-book exam” for LLMs: the model no longer answers from memory, but grounds its output in verifiable sources.

Embeddings: the choice that changes everything

Not all embeddings are equal! For effective RAG, use models adapted to your domain: BGE, E5, OpenAI, Cohere, or specialized models (legal, medical…). Frameworks like LlamaIndex, Haystack, LangChain make it easy to test and integrate different encoders.

Chunking: the art of splitting without losing meaning

Bad chunking kills relevance: too large, retrieval is fuzzy; too small, you lose context. Use structure-aware chunking (titles, sections) with 30–50% overlap to maximize recall. Tools like LlamaIndex and Haystack offer advanced chunking modules.

Retrieval and re-ranking: double insurance

A good RAG pipeline first retrieves broadly (recall), then applies re-ranking (BM25, Cross-Encoder, Cohere Rerank…) to keep only the most relevant passages. Without re-ranking, LLMs can make bad context sound plausible—quality matters!

Chunk contextualization: the semantic boost

Add a title or mini-summary to each chunk before indexing: this improves relevance, especially for abstract queries. Modern frameworks integrate this step (LlamaIndex, Haystack, LangChain output parsers).

The 3 pitfalls to avoid

  1. Random chunking: too large = fuzzy, too small = loss of context
  2. Generic embeddings: ineffective on jargon, tables, acronyms
  3. No citations: impossible to distinguish grounded from hallucinated answers

Best practices for a reliable RAG

  • Structure-aware chunking with overlap
  • Specialized embeddings tested on your data
  • Systematic re-ranking
  • Enforced citations and output format (JSON, markdown, etc.)
  • Evaluate retrieval independently from the LLM (recall@k, citation quality)

Beware of noise: too much context kills relevance

Injecting too many or the wrong chunks drowns the model and anchors generation on bad sources. Always measure recall@k and adjust the number of injected chunks.

  • LlamaIndex: full RAG pipeline, advanced chunking, built-in evaluation
  • Haystack: retrieval, re-ranking, multiple connectors
  • LangChain: orchestration, output parsers, structured citations
  • Weaviate, Qdrant, Milvus: high-performance vector databases
  • Cohere Rerank, Cross-Encoder: state-of-the-art re-ranking

For hands-on practice: scripts and notebooks at https://github.com/alouani-org/mecanics-of-llms

Engineer’s method: diagnose and improve your RAG

  1. Separate retrieval and generation: is the answer in the retrieved chunks?
  2. Measure: recall@k, citation quality, injected noise
  3. Improve in order: structured chunking, domain embeddings, re-ranking, output/citation constraints

FAQ

Why does my RAG hallucinate even with documents injected? Because only relevant, readable, and well-used passages matter. Noisy or excessive context is ignored or misused.

How many chunks should I inject? Start small (3–5) with re-ranking, then adjust based on retrieval quality.

For more: vector databases, embedding selection, advanced indexing, evaluation, and pitfalls are detailed in The Mechanics of LLMs (Augmented Systems & RAG chapter).

Tags :
Share :

Related Posts