1. Define what the assistant can answer
Start with a bounded collection such as internal operating instructions or approved product documentation. Specify the audience and the kinds of questions that belong within scope. An assistant built to explain an internal procedure should not drift into giving individual legal, financial or medical advice simply because a user asks a fluent question.
Identify the document owner and the authoritative source for each subject. Remove duplicate drafts or mark them clearly. Contradictory documents are not solved by adding a chat interface. The service should preserve document titles, source locations and revision information so users can inspect the material behind an answer rather than treating generated text as the record itself.
2. Understand retrieval before generation
Retrieval-augmented generation, commonly called RAG, combines document search with text generation. The system first retrieves relevant passages, then supplies those passages to a language model alongside the question. This can help ground an answer in a chosen collection. It does not guarantee that the right passage is retrieved or that the model interprets it correctly.
Keyword search is useful for exact references, product codes and unusual names. Semantic search uses mathematical representations of text, called embeddings, to find material with related meaning. A combined approach may suit mixed document collections. Evaluate retrieval separately from the final answer: if the relevant paragraph never reaches the model, prompt wording alone cannot reliably repair the result.
3. Preserve context and permissions
Documents are often divided into smaller passages for indexing. Keep enough context to preserve headings, exceptions and relationships between sections. A sentence describing eligibility may depend on an exclusion in the next paragraph. Tables need particular care because separating a value from its row label can change its meaning.
Apply access rules before content is retrieved and before it is sent to a model. A user should not gain access to restricted information through a generated summary. Preserve permissions as documents enter the index, and update them when access changes. Deleting a source should trigger appropriate removal from search indexes, caches and other retained copies.
4. Compare hosting choices
Hosted model APIs from providers such as OpenAI and Anthropic can be considered as part of an architecture. Their suitability depends on contract terms, supported controls and the needs of the task. Check the provider’s documentation for data handling, regional processing options and service limits before making a selection.
Self-hosting a model can give an organisation more direct control over its infrastructure, but also requires capacity planning, security maintenance and deployment expertise. Open-weight does not automatically mean unrestricted use: examine the specific model licence. Compare total operating effort, response quality on your questions, latency and the ability to change providers, rather than choosing on a broad label alone.
5. Make unsupported answers visible
Require source references that lead to the material actually used. A citation should support the associated statement, not merely point to a related document. When the collection contains no answer, the assistant should say so and offer a defined next step, such as contacting the document owner. A confident guess is not a substitute for missing policy.
Evaluate questions with straightforward answers, questions requiring several passages and questions outside scope. Include outdated documents, contradictory sources and wording designed to override the assistant’s instructions. Retrieved text may contain hostile instructions, so it must remain evidence rather than authority. Test whether the system leaks restricted content or invents sources when pressured.
6. Plan ongoing ownership
An assistant needs a document update process as well as a model integration. Agree who approves new sources, how indexing failures are reported and how users flag an unhelpful answer. Keep logs proportionate: questions may contain personal or confidential information, even when the source collection does not. Limit access and retention accordingly.
A scoped build can include source ingestion, permission-aware retrieval, an answer interface and an evaluation set. Before launch, decide who reviews failures and who can pause access. Measure whether users can complete the intended task with appropriate source checking. A conversational interface is useful only when the underlying information and operational responsibilities remain dependable.