Retrieval-augmented generation (RAG) has become the default architecture for enterprise AI assistants. It grounds a large language model in your own knowledge, reduces hallucination and keeps answers current without retraining.
But the gap between a demo and a dependable system is wide. These are the lessons we see repeatedly.
1. Evaluation comes before architecture
Before choosing a vector database or a chunking strategy, build a golden dataset: a few hundred real questions with expert-approved answers. Without it, every design decision is opinion.
- Measure retrieval quality (did we fetch the right passages?) separately from generation quality (did we answer faithfully?).
- Re-run the evaluation on every change to prompts, models or indexes.
2. Chunking is a product decision
Splitting documents every 500 tokens is easy and usually wrong. Structure-aware chunking — by heading, clause or table — preserves meaning. Policies, contracts and manuals each deserve their own strategy.
3. Hybrid search beats pure vectors
Semantic search misses exact identifiers like product codes, error numbers and names. Combining keyword (BM25) and vector retrieval, followed by a re-ranker, consistently improves relevance.
4. Permissions must travel with the data
If a user cannot open a document, the assistant must not quote it. Apply access control at retrieval time, using the same identities as your source systems.
5. Citations build trust
Answers should link to their sources. Citations let users verify, and they give your team a fast way to debug wrong answers.
The most persuasive feature of an enterprise assistant is not eloquence — it is the footnote.
6. Plan for "I don't know"
A good assistant declines when the evidence is weak. Tune thresholds so low-confidence questions route to a human or a search result rather than a confident guess.
7. Operate it like software
Monitor latency, cost per answer, feedback scores and retrieval hit-rates. Version prompts and indexes. Treat the assistant as a living product with an owner and a roadmap.
The bottom line
RAG succeeds when it is engineered, not just assembled. Start with evaluation, respect permissions, and measure relentlessly — and your assistant will earn the trust it needs to scale.
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- #RAG
- #LLM Evaluation
