How do I connect an LLM to our internal docs so it answers from our data only?

To make an LLM answer from your internal docs only, you usually build a RAG system: Retrieval-Augmented Generation.

What that means

Instead of letting the model rely on its pretraining, you:

1. Store your docs in a searchable index
2. Retrieve the most relevant passages for each question
3. Give those passages to the LLM
4. Instruct the LLM to answer only from the provided context

---

Typical architecture

Internal docs → chunking → embeddings → vector database / search index → retrieve top passages → LLM prompt → answer

---

Step-by-step

1) Collect and clean your docs

Pull in PDFs, wiki pages, Notion, SharePoint, Confluence, Google Docs, etc.

Then:

• remove boilerplate if needed
• preserve titles, headings, and metadata
• keep source links, timestamps, permissions, and document IDs

2) Split docs into chunks

LLMs work better with smaller text sections.

Common approach:

• chunk size: ~300–800 tokens
• overlap: ~50–150 tokens

Keep chunks semantically coherent if possible, e.g. by heading/section.

3) Create embeddings

Turn each chunk into a vector using an embedding model.

Store:

• chunk text
• embedding vector
• metadata: source, title, section, ACLs, updated_at, etc.

4) Put them in a retrieval system

Options:

• Vector DB: Pinecone, Weaviate, Milvus, Qdrant, pgvector
• Hybrid search: combine keyword + vector search
• Enterprise search: Elasticsearch/OpenSearch with vectors

Hybrid search is often best for internal docs because exact terms matter.

5) Retrieve relevant chunks at question time

When a user asks a question:

• embed the question
• search the index
• return top-k relevant chunks
• optionally rerank them with a stronger model

6) Prompt the LLM with strict instructions

Tell the model:

• answer only using the supplied context
• if the answer isn’t in the context, say “I don’t know”
• cite sources
• don’t use outside knowledge

Example instruction:

Use only the provided context to answer the question. If the answer is not contained in the context, say you don’t have enough information. Do not guess.

7) Add citations

Return:

• answer
• source snippets
• document links
• chunk IDs

This builds trust and makes auditing easier.

---

Important guardrails

Enforce permissions

If docs have access controls, retrieval must respect them.
Don’t let the model see chunks the user shouldn’t access.

Limit the context

Only provide the most relevant chunks. Too much context can confuse the model.

Use “abstain” behavior

If retrieval finds nothing strong enough, the system should respond:

• “I couldn’t find that in the docs”
• and optionally suggest related docs

Log retrieval results

Store:

• query
• retrieved chunks
• final answer
• citations

Useful for debugging and evals.

---

A simple prompt pattern

System message

You are a helpful assistant for internal documentation. Answer only using the provided context. If the answer is not in the context, say you don't know. Cite sources.

User message

Question: How do I reset my VPN token?

Context: [chunk 1] [chunk 2] [chunk 3]

Expected behavior

• Answer from the chunks
• Quote or paraphrase accurately
• Cite the document section

---

If you want “data only,” note this caveat

No LLM can be perfectly guaranteed to “know nothing else” in the absolute sense, because its base model still contains prior knowledge. But you can make it behave as if it only knows your docs by:

• using RAG
• strict prompts
• citations
• answer refusal when context is missing
• optionally using a smaller local model or fine-tuned model for stronger control

For high-stakes use cases, add:

• answer verification
• citation checking
• policy rules
• human review for certain queries

---

Common stack choices

Fast to build

• Loader: LangChain / LlamaIndex
• Embeddings: OpenAI, Cohere, sentence-transformers
• Vector DB: Pinecone / Qdrant / pgvector
• LLM: GPT-style model via API

More enterprise/self-hosted

• Ingestion pipeline: Airflow / Dagster
• Search: Elasticsearch/OpenSearch + vectors
• Vector DB: pgvector/Qdrant/Milvus
• LLM: hosted private model or on-prem

---

Minimal pseudo-flow

doc -> chunk -> embed -> index

question -> embed -> retrieve top chunks -> prompt LLM with chunks -> answer

---

Best practices

• chunk by section, not arbitrary length only
• add metadata and source links
• use hybrid retrieval + reranking
• enforce ACLs in retrieval
• return citations
• tune with real user questions
• evaluate on a test set of known Q&A

---

If you want, I can also give you:

1. a reference architecture diagram,
2. a Python example using LangChain or LlamaIndex, or
3. a production checklist for secure internal-doc QA.