Retrieval-Augmented Generation, or RAG, combines information retrieval with a language model. The system retrieves relevant material and supplies it as context when asking the model to prepare an answer.
This can make company documents available at question time without retraining the model for every update. It does not guarantee that the retrieved material is correct or that the generated answer faithfully reflects it.
The workflow from source to answer
Prepare and index sources
An ingestion process reads approved sources, extracts text and retains metadata such as the document owner, version and access rules. Documents may be split into passages. Retrieval can use keyword indexes, embeddings, structured filters or a combination of these.
Source integrations need to handle updates, deletions and permission changes. A stale index can produce an answer from a document that has already been replaced or withdrawn.
Retrieve relevant material
The system interprets a question and searches for candidate passages. It may rerank them, combine results or ask for clarification. Relevant passages must also be authorised for the user; access checks should happen before restricted content reaches a model or response.
Generate and verify the response
The selected context and question are passed to a language model. Instructions can require citations, limit the answer to available evidence and define refusal behaviour. These controls need testing: a model may still omit a qualification, misread a passage or make an unsupported claim.
The interface should let users open the original source. A citation is useful only when the cited passage supports the associated statement.
RAG, search and fine-tuning
| Approach | Main purpose | Important limitation |
|---|---|---|
| Full-text search | Locate documents or passages using words and identifiers | Different wording can make relevant material harder to find |
| Semantic search | Rank material by learned semantic similarity | Similarity is not proof of relevance or factual correctness |
| RAG | Prepare an answer using retrieved context | Retrieval and generation can both introduce errors |
| Fine-tuning | Adapt model behaviour through additional training | Training does not provide a live, authoritative document index |
RAG does not change model weights. Fine-tuning does. They can be combined, but neither is automatically the right starting point. A well-organised search interface may solve a task without generated answers.
Where RAG may help
The following are illustrative use cases, not reports of measured client results.
- Internal procedures: find the current leave or escalation policy and show the relevant section.
- Customer support: retrieve known issues and diagnostic steps for an agent to review.
- Legal research: locate candidate clauses while keeping legal interpretation with an appropriately qualified reviewer.
- Technical documentation: connect an engineering question with relevant specifications, decision records or code documentation.
- Sales support: locate approved product information and examples without inventing capabilities or customer outcomes.
For each use case, define the authoritative source and the consequence of an incorrect answer. An application used to inform a sensitive decision needs different review requirements from a tool that helps someone find a document.
Measure the problem before claiming savings
Record how people currently complete the task: search time, unsuccessful searches, corrections and escalations. A pilot should compare the new workflow with that baseline, including the time spent checking generated answers.
Potentially released time is not automatically a cash saving. Whether it becomes additional output, reduced overtime or another measurable benefit depends on how the organisation changes its work.
Data quality and access controls
A RAG system needs information that can be used lawfully and interpreted reliably. Remove or flag duplicates, retain effective dates and identify conflicting sources. The system should know when the available material cannot answer a question.
Permissions need to cover documents, indexes, embeddings, retrieved passages and generated responses. Consider whether a response can reveal the existence of a restricted document even when it does not quote it.
Self-hosting is one deployment option. Hosted models and services are another. Map data flows and contractual conditions for the chosen architecture; regulations do not impose a single deployment model for every organisation.
Evaluate quality and failure behaviour
Build an evaluation set with answerable, unanswerable and adversarial questions. Include realistic abbreviations, spelling variations and languages. Test whether the system:
- retrieves the passages needed for an answer;
- respects user permissions;
- cites evidence that supports its claims;
- handles conflicting or outdated documents;
- declines unsupported requests;
- remains useful after source and model changes.
Automated checks can help, but human assessment is needed for cases where relevance and interpretation matter. Set acceptance thresholds for the particular task rather than use a universal hallucination-rate target.
Budget and delivery planning
Costs include source preparation, integrations, indexing, model usage, evaluation, interfaces, monitoring and maintenance. Usage volume, document updates and latency requirements affect operating costs.
A sensible sequence is to define a task, inspect its sources, test a limited prototype, evaluate the results and then scope a production system. Access controls, monitoring and support should be designed before wider rollout. Scope and dates are agreed for the project; a prototype is not evidence of production readiness.
BeHive and related research work
BeHive is QA10’s MIT-licensed open-source research engine. Its documented purpose covers source-based research, claim assessment and relationships between entities. It can be installed with pip install behive.
BeHive should be assessed as a research tool for the intended task. This does not establish a ready-made enterprise RAG platform, source connectors, access-control integrations or a guaranteed deployment schedule.
FAQ
Does RAG prevent hallucinations?
No. Retrieval can provide relevant evidence, but the model can still produce unsupported or misleading statements. Evaluate answer quality, citation support and refusal behaviour on the intended tasks.
How long does a RAG project take?
The schedule depends on source access, data preparation, integrations, security requirements and acceptance criteria. Establish these during scoping and use a limited pilot to test uncertain assumptions.
Can a RAG system use confidential data?
That depends on the permissions, lawful processing basis, infrastructure and service arrangements. Map the complete data flow, including models, logs and backups, and assess the implementation with the relevant data and security owners.
What sources can it use?
Potential sources include approved documents, websites and structured records, provided suitable extraction and access mechanisms are implemented. Support for a format or repository must be checked in the chosen system rather than assumed.
Does RAG work in Polish?
It can. Evaluate retrieval and answer quality using representative Polish documents, inflected forms, abbreviations and domain terminology. Language support alone does not establish acceptable performance for a particular collection.
Plan the next step
For a business process review and software developed from its findings, explore Q-Journey. For a defined software requirement, explore Q-Dev. You can also contact us with the task and sources you want to discuss.