When your documents need better answers.
RAG retrieves relevant material before asking a language model to produce an answer. It can help with internal knowledge search, document assistants, domain-specific question answering, and research tools. It is useful when the answer must reflect a changing corpus or cite material your team can inspect.
Retrieval does not make every answer trustworthy. Missing documents, poor parsing, weak search, and ambiguous questions can still produce convincing errors. I define what the system can answer, when it must refuse, and how an operator can trace a response back to its source.
Retrieval first. Architecture second.
I begin with representative questions and an evaluation set. A lexical baseline establishes what ordinary search can already solve. Dense or hybrid retrieval, reranking, and more complex generation are added when measured errors justify them.
That process matters for multilingual or low-resource material. A model that performs well on an English benchmark may not handle your language, document format, or domain vocabulary. I compare the retrieval paths against your corpus instead of selecting infrastructure by trend.
- Corpus inventory, parsing, provenance, and document updates
- Chunking and metadata designed around the source material
- Lexical, dense, and hybrid retrieval comparisons
- Grounded answers with citations and explicit refusal behavior
- Evaluation of retrieval quality, answer support, latency, and cost
From experiment to an application.
A working RAG prototype is only part of the delivery. The application needs authentication and access boundaries, ingestion updates, API contracts, error handling, and a way to investigate bad answers. I connect retrieval and generation to the surrounding product and deployment workflow.
The engagement can cover a focused retrieval audit, a new knowledge application, or an existing LLM integration that needs evaluation and operational controls. Deliverables are scoped around the corpus, user roles, supported questions, and the quality and cost limits your team agrees to operate.
- A measured baseline and a documented evaluation set
- An ingestion and retrieval path with source traceability
- Application and API integration with access controls
- Failure analysis, deployment notes, and ownership handover
Research you can inspect.
NepSAUL explores Nepali legal question answering across 10,320 case laws. Its reported 91% Precision@1 is a BM25 retrieval result, not a promise of production answer accuracy. The project demonstrates why a simple measured baseline belongs before a more elaborate retrieval architecture.
My research archive also covers multilingual sentence embeddings and low-resource language evaluation. Those records include methods, results, and limitations so you can judge the work behind this service.
Discuss a RAG or LLM project.
I work remotely from Lalitpur in the Kathmandu Valley with founders and engineering teams in Nepal and worldwide. For a useful first conversation, describe who will ask questions, what documents they can access, the languages involved, and the consequences of an unsupported answer.
Share corpus size, update frequency, expected traffic, and any deployment constraints when known. An initial audit establishes whether RAG is the right tool and what a credible evaluation should measure. If your main problem is moving information between tools, the AI automation service may be the better starting point.