Retrieval-augmented generation looks deceptively simple in a demo. The trouble starts when the corpus grows and real users ask questions your chunking never anticipated. Most "the AI is wrong" complaints are retrieval bugs, not model bugs.
The first thing to get right is chunking. Splitting on a fixed token count shreds tables and procedures — exactly what people ask about. Chunk on structure first and fall back to token windows only inside long sections.
Embeddings are the second trap. A general-purpose model may not separate near-duplicate documents well, so the closest chunk is confidently irrelevant. Measure retrieval quality with a small labelled set before you blame the model.
Key takeaways
- Chunk on document structure before falling back to token windows.
- Attach source and section metadata to every chunk so you can cite and debug.
- Build a labelled eval set of real questions; measure retrieval before model quality.
- Cache embeddings; re-embedding the whole corpus each deploy is wasteful.
- Show citations in the UI to build trust and surface wrong answers.
Practical checklist
- Create a 50-question evaluation set from real queries.
- Log every retrieval (query, chunks, scores) for the first month.
- Add a helpful/not-helpful control and review misses weekly.
- Define a low-confidence fallback message instead of guessing.
What to do next week
If you are adding an AI feature, start with retrieval quality and observability, not the prompt. We have built systems like this for teams in India and abroad, and are happy to review your plan in a short call.
How we work with clients at TechTrio
Every engagement at TechTrio Automation starts with a short discovery phase: we map your current stack, traffic, conversion paths, and operational bottlenecks. From there we propose a phased roadmap — quick wins first (tracking, analytics hygiene, performance, or a focused automation), then deeper builds (product modules, integrations, or marketing systems). Our teams in Ahmedabad and Mehsana collaborate closely with stakeholders in India, the UK, USA, Canada, and the UAE, so documentation, handoffs, and support hours stay practical.
We bias toward maintainable defaults: typed frontends where it pays off, predictable hosting on Vercel or similar for marketing sites, Firebase or Postgres depending on data and compliance needs, and observability so you are never guessing whether a workflow ran. Security is not an afterthought — least-privilege access, secrets outside the repo, and reviews for anything that touches payments or personal data.
