model · OpenAI
RAG & Knowledge Retrieval with OpenAI GPT
RAG & Knowledge Retrieval built on OpenAI GPT, chosen where it genuinely fits, and swapped where it does not.
- Category
- model
- Vendor
- OpenAI
- Alternatives we also use
- 9
Why OpenAI GPT for this
Orqent Labs builds RAG systems where accuracy is measured against a labelled question set, so you know the number rather than trusting a vibe.
OpenAI GPT is strongest at the widest tooling ecosystem and mature structured-output support. For rag & knowledge retrieval that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: cost at scale, and a data-handling posture that some regulated buyers will not accept without an enterprise agreement. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.
We hand over with runbooks, tests and a team that knows how it works, not a dependency.
The honest assessment
- What it is
- OpenAI's GPT family, broad ecosystem support and strong general performance.
- Strongest at
- the widest tooling ecosystem and mature structured-output support
- Trade-off
- cost at scale, and a data-handling posture that some regulated buyers will not accept without an enterprise agreement
- Category
- model
We are not a reseller for OpenAI and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Ingestion pipeline for your real document formats
- Chunking and embedding strategy tuned to your corpus
- Hybrid keyword plus vector retrieval with reranking
- Citations on every answer, traceable to the source page
- Permission-aware retrieval that respects existing access rules
- Retrieval quality benchmarked against a labelled question set
Questions
RAG or fine-tuning?
RAG for knowledge that changes and must be cited; fine-tuning for style, format and task behaviour. Most production systems use RAG for the facts and light fine-tuning or few-shot prompting for the form.
How accurate will it be?
We build a labelled question set from your domain and report retrieval precision and answer accuracy against it. That number is the deliverable. We do not ship a system whose quality nobody has measured.
Can it respect our existing permissions?
Yes. Retrieval is filtered by the user's actual entitlements, so the assistant can never surface a document the user could not already open.
Alternatives for rag & knowledge retrieval
Same capability, different stack. Each page states its own trade-off.
Building with OpenAI GPT?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878