RAG System Development

A RAG system is for a team that already has the answers and cannot afford a model that invents them. Support teams sitting on years of tickets, clinics with treatment protocols, law firms with matter files, and product companies with a private knowledge base all have the same problem. People ask the same questions, the answer lives in a document or a database, and a general chatbot will sound confident while being wrong.

Armor Tech builds these systems for operations leaders, knowledge managers, and product teams who need answers grounded in their own material. The buyer is usually the person responsible for accuracy: a head of support, a clinical or compliance lead, or a founder putting an assistant inside a product their customers already pay for. The fit is strong when the source of truth exists, it changes, and the person reading the answer needs to see where it came from.

It is the wrong project when the business wants an agent that takes actions in other systems. That is a different service. RAG is the retrieval layer: find the right passage, rank it, and answer with a citation. If the documents are missing, contradictory, or known only to one person, the first job is to collect them. A model cannot retrieve what was never written down.

Retrieval-augmented generation connects a language model to private data so the answer is assembled from that data instead of from the model's memory. Armor Tech designs the pipeline that makes this reliable: how documents enter, how they are cut into pieces, how a question finds the right pieces, and how the answer points back to the source. The model can be swapped later. The index and the citation rules are the product.

Vector database setup is the store for those pieces. Pinecone, Weaviate, or pgvector is chosen from how the data is already hosted and how often it must be filtered by customer, site, or permission. A clinic and a hospital group do not share one index. Each query is scoped to the records that user is allowed to see. Optimization here means the index stays fast as the library grows, and stale chunks are removed when a document is replaced.

Document ingestion pipelines turn files into searchable pieces. PDFs, policies, tickets, spreadsheets, and pages from an internal wiki each need a different path. The pipeline records which file a chunk came from, which version, and when it was indexed. A new upload does not silently sit beside last year's version of the same policy. Failed files are reported. They are not dropped.

Hybrid search combines dense vectors with sparse keyword search. Vectors find passages that mean the same thing in different words. Keyword search finds the exact code, drug name, clause number, or error string that a vector search often misses. Armor Tech uses both, then reranks the merged list so the model sees a short set of strong passages instead of a pile of near-misses. Context compression keeps that set inside the model's limit without cutting the sentence that actually answers the question.

Citation and source tracing are required on every answer that will be shown to a customer or a staff member. The response names the document, the section, and, where the source allows it, the page. If the retrieved passages do not support an answer, the system says so. It does not fill the gap with a plausible sentence. That refusal is part of the design, because an uncited answer is not a successful run.

Continuous knowledge updates keep the index matched to the business. Policies change, prices change, and a product release retires an old help article. The pipeline re-indexes on a schedule or on a webhook from the source system. LlamaIndex and LangChain are used to assemble the retrieval chain. OpenAI models write the final answer from the passages they were given. The passages, not the model's training data, are what the citation points at.

The project starts with the questions people already ask and the files those answers should come from. Armor Tech collects a sample of real questions, the documents that should have answered them, and the cases where the current process got it wrong. Permission rules are written down at the same time. A search that returns another customer's record is a failure even if the text is relevant.

The first concrete deliverable is a retrieval design for one collection. It names the sources, how each source is split, which fields filter the search, what a correct citation looks like, and a small set of questions with the passage that should win. The team can read that design in one meeting and try the questions against the sample. Indexing the full library waits until those questions return the right passages.

Later slices add the remaining collections, the update job, and the place the answer is shown: a help desk, an internal tool, or a product screen. Monitoring covers empty results, answers with no citation, and queries that keep missing. The handoff includes the job that refreshes the index, so the system does not freeze on the day the project ends.