LLM Integration & RAG
LLM integration means connecting a language model to your data and your product so it answers from what you actually know rather than what it was trained on. In practice that is retrieval-augmented generation: find the right context, give it to the model, and cite where the answer came from. Most of the engineering effort goes into retrieval quality, not into the model call.
Retrieval is where RAG succeeds or fails
If the right passage is not retrieved, no model can answer correctly. Teams typically discover this after blaming the model for months.
- Chunking that respects document structure rather than cutting at a fixed length.
- Hybrid search — keyword alongside vectors, because exact terms still matter.
- Reranking the candidate set before it reaches the model's context.
- Retrieval measured on its own: was the right passage in the top results at all?
Making outputs safe to use programmatically
A model that returns prose is fine for a chat window and useless as an input to the rest of your system. When the output feeds code, it needs to be structured and validated.
- Schema-constrained outputs, validated before anything downstream consumes them.
- Explicit handling for the model declining or returning nothing usable.
- Citations back to source documents, so a user can check the claim.
- Deterministic fallbacks when the confident answer is not good enough to ship.
Keeping the index current
A retrieval system quietly decays as the underlying documents change. We build the ingestion pipeline as a first-class part of the system — incremental updates, deletion handling, and a way to tell how stale the index is — because a confidently wrong answer sourced from a deleted document is worse than no answer.
Frequently asked questions
- What is RAG in plain terms?
- Retrieval-augmented generation: before answering, the system searches your documents for relevant passages and includes them in the model's context. The model then answers from that material and cites it, instead of relying on what it memorised during training.
- Can it answer from our private data without that data training the model?
- Yes. Retrieval puts your data into the request, not into the model's weights. Whether the provider retains request data depends on the provider and plan, which is a contract question we can help you check before you commit.
- Which vector database should we use?
- For most workloads, PostgreSQL with pgvector is sufficient and avoids adding a component to operate. Dedicated vector databases start to earn their place at large scale or with demanding filtering. We would rather add one when your numbers justify it.
- How do we know retrieval is working?
- By measuring it separately from generation. We build a set of questions with known correct sources and track whether those sources appear in the retrieved results. That number tells you where to invest — most of the time it is retrieval, not the model.
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