Retrieval-Augmented Generation (RAG) Systems

Retrieval-Augmented Generation is how an LLM answers from your documents instead of from its training data. It is also where most enterprise LLM projects quietly fail: the model is fine, but retrieval returns the wrong chunks and the answer is confidently wrong. IntelliSensei builds RAG systems as retrieval engineering problems first, in PyTorch, with evaluation at every layer.

Embedding-model selection and fine-tuning

General-purpose embedding models are trained on the open web; your corpus is not the open web. We benchmark candidate open-weight embedding models on your data, then fine-tune the best one contrastively in PyTorch with sentence-transformers, mining positive and hard-negative pairs from your search logs and documents. A fine-tuned embedding model routinely lifts recall@k by double digits on domain corpora, which is a bigger win than any prompt change. Our tutorial Fine-tuning an embedding model for better RAG retrieval shows the method.

Chunking and indexing strategy

How you split documents determines what can be retrieved. We design chunking around your document structure (headings, tables, code, forms), attach metadata that lets retrieval filter by product, date or access level, and handle multi-format ingestion (PDF, HTML, Office, tickets, transcripts) with layout-aware parsing rather than naive text extraction.

Vector-store integration

We are pragmatic about storage. For most teams pgvector inside the Postgres you already run is the right answer; OpenSearch fits when you already operate it and need hybrid search at scale; dedicated vector databases make sense at very high volume or when you need their specific features. We integrate whichever you choose and size the index (HNSW parameters, quantization) for your latency target.

Hybrid retrieval and rerankers

Dense retrieval alone misses exact identifiers, part numbers and rare terms. We combine dense and lexical (BM25) retrieval, fuse the results, and apply a cross-encoder reranker, fine-tuned on your data where it pays, to put the right passages at the top. This two-stage design is what separates a demo from a system people trust.

Evaluation

You cannot improve what you do not measure. We build a labelled evaluation set from your real questions and measure retrieval (recall@k, MRR) separately from generation (groundedness, faithfulness, answer relevance) so you know which half to fix. Evaluation runs in CI, so a change to chunking or a model upgrade cannot silently regress quality.

Guardrails and PII handling

Enterprise RAG has to respect document permissions, redact personal data and refuse out-of-scope questions. We enforce access control at retrieval time (a user only retrieves what they may read), scrub PII during ingestion, add citation so every answer points to its sources, and put output checks in front of the user.

Keeping indexes fresh in production

Documents change. We build incremental ingestion pipelines that detect updated and deleted sources, re-embed only what changed, and keep the index consistent, with monitoring for stale content and retrieval-quality drift.

Deliverables

An ingestion pipeline, a retrieval service, an evaluation suite with baseline and final scores, and runbooks for operating the system. Where generation is needed we serve the LLM with our inference and serving optimization practice, and where the model itself needs to change we apply our LLM fine-tuning service. Not sure which you need? Read Fine-tune, RAG or both?.

Ready to build a RAG system that answers correctly? Contact us to start with a retrieval audit of your current setup or a discovery call for a new one.

Back to services