LLM & RAG · RAG system development
Most RAG builds fail at retrieval, not generation. The language model is only as good as the passage it receives, and if the retrieval layer returns the wrong paragraph — or the right document in the wrong chunk — the answer will be confident and wrong. Building a RAG system means engineering the retrieval path first, testing it on your hardest questions, and only then handing the result to a model.
Banao designs and builds RAG systems end-to-end: corpus preparation, chunking strategy, embedding and indexing, hybrid search, re-ranking, answer grounding, citation threading, and the evaluation harness that proves the whole pipeline is doing what you think it is. We do not hand over a notebook demo.
Banao— we run an internal RAG system over our own runbooks and engineering decisions, every working day, and our engineers stake daily decisions on its answers.
A RAG system in production is an ingestion pipeline, a retrieval layer, a grounding contract, and an evaluation harness — all of them built together. We do not offshore the hard parts.
We map your documents to the questions they actually answer, then chunk each format the way it is written — a contract clause splits differently from a product description — so retrieval works on the structure of meaning, not on a token counter.
We select an embedding model matched to your domain vocabulary, build the index with the metadata your retrieval needs, and set up incremental updates so a document changed this morning reaches the index this afternoon.
Keyword recall catches exact matches; semantic search catches paraphrased intent; a re-ranker orders the result by genuine relevance before the passage reaches the model. Most accuracy gains we ship come from this layer.
The model is constrained to answer from the retrieved passage and to identify the source, so every answer carries a citation a reviewer can open and check, and the system can abstain rather than invent.
We build a retrieval-quality and faithfulness eval suite from your real questions and known-good answers, run it before launch, and keep it running on every change — so accuracy is a number you can track, not a feeling from a demo.
OCR for scanned files, parser chains for PDFs and spreadsheets, de-duplication for contradictory versions, and incremental re-indexing tied to your document lifecycle — so the system reflects current truth, not a stale snapshot.
Retrieval is gated to the documents each user is allowed to see, sensitive fields are redacted before they enter an embedding or a model call, and the permission model follows your existing directory — not a second list to maintain.
Live retrieval-quality metrics, answer-faithfulness sampling, and alerting when a recent document change breaks a previously passing eval case — so the system stays accurate as the corpus grows and changes.
A RAG system's accuracy is set before the model ever runs. By the time a language model produces an answer, the decision that mattered — was the right passage retrieved? — was already made. Teams that tune the prompt for days while leaving retrieval unscored are polishing a surface that rests on bad plumbing.
Four decisions account for most of the difference between a RAG system a team trusts and one that gets quietly routed around:
The right chunk is the unit of meaning the question resolves against — which is different for a legal clause, a product spec, and a support log. We map question type to document structure before writing a single line of ingestion code.
Semantic search misses exact model numbers, product codes, and named entities; keyword search misses paraphrased intent. A hybrid approach with a re-ranker at the top catches both, and the accuracy improvement on your actual question set is measurable before you ship anything.
A RAG system that must always answer will always answer — including when the corpus has nothing relevant. We build the abstention path first because a system that says "I don't have that" is safer and more trusted than one that fills gaps plausibly.
We do not declare a RAG system ready because it passed a hand-chosen demo. We build a retrieval-quality and faithfulness suite from your real questions, score it before launch, and treat any regression as a blocker — because a broken eval is the first sign of a system drifting toward unreliable.
Most RAG architecture questions do not have one right answer; they have a right answer given your document types, your query patterns, and the cost you can bear. We surface these choices explicitly in the Discovery Sprint rather than burying them in an implementation and hoping you do not ask.
The choices below are the ones that most often get decided by convenience and later regretted as the system scales past its first hundred users:
The choice between pgvector on your existing Postgres, a managed cloud vector DB, or a dedicated store like Weaviate or Qdrant depends on your query throughput, your index size at two years out, and whether you need metadata filtering that SQL already handles well. We size this from your numbers, not from hype.
If your documents update continuously — tickets, changelogs, live pricing — you need an event-driven ingestion path that re-indexes deltas, not a nightly batch job that leaves a day-old truth in production.
When users should only see documents they are permitted to read, the access model belongs inside the retrieval query — not in a post-filter on the answer. We wire this to your identity provider so permission changes propagate without manual index surgery.
Every passage included in the prompt costs tokens and risks diluting the passage the model should actually attend to. We set context budgets by question type, trim redundant passages before they reach the model, and measure whether the extra context is improving or hurting faithfulness.
Metrics shown dotted (··) are being finalised in our case-study metrics pack and will be published once verified. The deployments are live and in daily use.
Majra (UAE)
We built a retrieval system for Majra that retrieves from its own published content in both English and Arabic and attaches the source to every answer. The system does not reach the open web — it answers from the organisation's own material, which is what makes the answer authoritative rather than speculative.
Studylab AI
For Studylab AI we engineered retrieval across the approved curriculum so answers stay inside the syllabus and carry the lesson reference — which is the bar a teacher needs before trusting an AI tool in a classroom.
Enterprise services firm (anonymized)
An internal retrieval system answers across ten years of policy documents, product guides, and closed tickets, cites the source for each answer, and routes to a named expert any question it cannot ground. The citation requirement is what made adoption stick — people stopped doubting the answers once they could check them.
Banao's internal RAG assistant answers our ~300 engineers from our own runbooks, architecture decision records, and codebase documentation — cited, with a link to the source. When a question cannot be grounded in those documents, the system routes to the person who owns the decision rather than guessing.
That system has to survive real use by people who know what the right answer is and will notice when it is wrong. The things we learnt from running it — which chunking assumptions fail at volume, where abstention is harder to tune than it looks, how citation trust is built or lost over weeks of use — are built into every RAG system we now ship.
We would rather name these on the first call than discover them after a Sprint:
We do not quote a RAG system build from a brief. We test retrieval feasibility on your actual documents and hardest questions first, so the design is grounded in measured results.
We run your real questions against your documents, score retrieval quality and faithfulness on your hardest cases, and hand back a RAG architecture design, an eval plan, and an ROI model — yours to keep whether or not you continue. If you proceed, the Sprint cost is credited against the build.
We build ingestion, chunking, embedding, hybrid search, re-ranking, grounding, citation threading, and the evaluation harness together — evaluation is a deliverable from day one, not a step bolted on after the demo passes.
We deploy with monitoring, incremental re-indexing, and the eval suite running on every change — so the system stays accurate as your corpus grows and you catch a retrieval regression before your users do.
What is RAG system development?
RAG system development is the engineering work of building a retrieval-augmented generation pipeline — corpus preparation, chunking, embedding, vector indexing, hybrid search, re-ranking, answer grounding, citation threading, and an evaluation harness — so a language model answers accurately from your own documents rather than from its training data.
Why do RAG prototypes fail on real user questions?
Almost always because of retrieval, not generation. The model can only use the passage it receives; if the retrieval layer returns the wrong chunk — or the right document but the wrong section — the answer will be confidently wrong. Naive chunking, missing metadata, and no abstention path are the three most common causes of RAG systems that pass the demo and fail in production.
What is the difference between vector search and hybrid search in a RAG system?
Vector search finds passages by semantic meaning — it catches paraphrased intent. Keyword search catches exact terms: product codes, named people, specific model numbers. A hybrid approach runs both, then uses a re-ranker to order the combined result by relevance. For most enterprise corpora, hybrid retrieval outperforms either alone, and the improvement is measurable on your own questions before you build.
How do you decide how to chunk documents for a RAG system?
By the unit of meaning the question resolves against in each document type. A contract clause, a product specification, and a support ticket each need a different chunking strategy — splitting everything into fixed 500-token blocks severs clauses from their context and cuts tables in half. We map your document types to question patterns before writing a line of ingestion code.
How do you stop a RAG system from making up answers?
Three mechanisms: grounding constrains the model to answer only from retrieved passages; citation threading requires it to identify the source; and abstention logic lets the system decline when the corpus has nothing relevant. We build all three, and we score the system's abstention behaviour as part of the evaluation harness — because a system that always answers is a system that sometimes invents.
How do you measure whether a RAG system is accurate?
We build a retrieval-quality and faithfulness eval suite from your real questions and known-good answers. Retrieval quality asks: was the right passage retrieved? Faithfulness asks: did the answer stick to the retrieved passage, or drift from it? Both scores run before launch and after every change, so accuracy is a tracked metric rather than an impression from the last demo.
How do you keep the RAG index current as documents change?
We build incremental re-indexing into the ingestion pipeline so changes to existing documents and additions of new ones propagate to the vector index without a full rebuild. For corpora that change continuously — ticket backlogs, pricing sheets, live policies — we use event-driven ingestion so a document updated this morning is retrievable by this afternoon.
How long does it take to build a RAG system?
A common path is a 2-week Discovery Sprint to test retrieval feasibility on your real documents, then a 6–10 week build, then a staged rollout. Banao's ~300-engineer bench means development begins in weeks. The Sprint is credited against the build if you continue — and it produces the architecture and ROI evidence you need to make the decision with data.
In 45 minutes we will tell you whether a RAG system can retrieve the right passage accurately enough to trust, and what a production build would take. Bring the questions that stump your team.
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