What is the difference between RAG and Fine-tuning?

RAG connects an LLM to external knowledge (like your company wiki) by retrieving relevant data at runtime. It's best for dynamic, fact-based queries. Fine-tuning modifies the model's internal weights to teach it a specific style, tone, or task format. RAG gives knowledge; Fine-tuning gives behaviour.

How do I reduce hallucinations in RAG?

Key strategies include: 1) Improve retrieval quality (Hybrid Search, Re-ranking), 2) Use 'Cite Sources' prompting, 3) Implement 'Self-Correction' loops where the model verifies its answer against the chunks, and 4) Use specialised models trained to say 'I don't know' when context is missing.

GraphRAG combines vector similarity search with Knowledge Graphs. Instead of just finding chunks with similar keywords, it traverses relationships (e.g., 'Entity A is subsidiary of Entity B'). This enables 'multi-hop reasoning' and answering high-level summary questions that standard vector RAG fails at.

Should I chunk by paragraph or fixed token size?

Context-aware chunking (paragraph, markdown headers) is superior to naive fixed-token chunking. It preserves semantic meaning. In 2026, 'Semantic Chunking' using embedding similarity to detect topic boundaries is the gold standard.

What is the best vector database for enterprise?

It depends on your stack. For pure scale, Pinecone or Milvus. For deep integration with existing SQL data, pgvector (PostgreSQL). For hybrid search and rich filtering, Weaviate or Qdrant. Security and RBAC are the deciding factors for enterprise adoption.

RAG Enterprise Implementation: Architecting Knowledge-Aware AI in 2026

The Evolution of RAG

Retrieval Augmented Generation started as a simple hack: "paste the document into the prompt." It evolved into "Vector RAG" (chunk, embed, search). In 2026, we are in the era of Modular RAG.

RAG Architecture — Modular RAG Retrieval Pipeline

Modular RAG treats the retrieval pipeline as a composable workflow. It includes query rewriting, routing between different indexes (SQL vs. Vector vs. Graph), and iterative refinement.

Advanced Retrieval Patterns

Simple cosine similarity is rarely enough for enterprise queries.

1. Hybrid Search

Combines Dense Retrieval (Vectors/Semantic) with Sparse Retrieval (BM25/Keywords).
Why? Vectors are great for concepts ("healthy food") but bad for specific IDs or exact phrases ("SKU-12345"). Hybrid search gets the best of both worlds.

2. Re-ranking

A "Cross-Encoder" model (like Cohere Rerank) scores the top 50 retrieved chunks against the query with high precision, re-ordering them to ensure the context window contains only the most relevant data.

3. Query Transformation

Never trust the user's raw query. Use an LLM to rewrite it.
User: "Compare it to the other one."
Rewritten: "Compare the Q3 financial report to the Q2 financial report."

Semantic Chunking & Indexing

Garbage In, Garbage Out. If you split a sentence in half, semantic meaning is lost.

Recursive Character Split: Good baseline. Splits by paragraphs, then newlines, then spaces.
Document-Specific Split: Uses structure (HTML tags, Markdown headers) to keep sections together.
Semantic Split: Calculates embedding similarity between sentences and only breaks chunks when the topic shifts.

Parent-Child Indexing: Retrieve small chunks for precision, but feed the "Parent" (larger context) to the LLM for coherence.

The Rise of GraphRAG

Vector databases flatten the world into lists. Knowledge Graphs preserve structure.

GraphRAG uses an LLM to extract entities and relationships from text during indexing. At query time, it can traverse these relationships.

Example: "How does the supply chain risk in China affect our US production?"
Vectors might find articles about "China" and "US". GraphRAG follows the path:China Factory --supplies--> Component X --used_in--> US Product Y.

Evaluating RAG Systems

You cannot improve what you cannot measure. The RAG Triad is the standard framework:

Context Relevance

Did we retrieve the right chunks? (Precision/Recall)

Groundedness

Is the answer fully supported by the chunks? (No hallucinations)

Answer Relevance

Does the answer actually address the user's question?

Security & RBAC in RAG

Enterprise RAG must respect permissions. If a user cannot see a document in SharePoint, they must not be able to retrieve its chunks in RAG.

Solution: Embed Access Control Lists (ACLs) into the vector metadata.{ "content": "...", "allowed_groups": ["hr", "management"] }. Filter retrieval by the user's group membership before the vector search.

Reference Architecture

# Enterprise RAG Stack 2026

1. Ingestion: Unstructured.io (PDF/PPT parsing)
2. Orchestration: LlamaIndex / LangChain
3. Indexing: Hybrid (Pinecone + ElasticSearch)
4. Knowledge Graph: Neo4j
5. Retrieval: Hybrid Search + Cohere Re-rank
6. Generation: vLLM (Llama 3 70B) or GPT-4o
7. Evaluation: Ragas / Arize Phoenix

Conclusion

RAG in 2026 is a sophisticated data engineering discipline. It requires moving beyond "magic" demos to building robust, observable, and secure knowledge pipelines. By combining vectors, graphs, and hybrid search, enterprises can finally unlock the value of their proprietary data with Generative AI.