RAG (Retrieval-Augmented Generation) implementation
We implement RAG — an approach where the AI answers not 'from its head' but based on fragments found in your data: first the system finds relevant pieces (vector DB search), then the model forms an answer from them, with a source link. This is the foundation of honest AI assistants over your data. Honestly upfront: RAG noticeably REDUCES hallucinations but does NOT eliminate them fully — the model can still distort even what was found; answer quality directly depends on the quality of your data and search accuracy ('garbage in — garbage out').
RAG (Retrieval-Augmented Generation) implementation — overview
RAG (Retrieval-Augmented Generation) is an architecture that 'grounds' AI answers in your data: (1) the user's query is turned into an embedding (894) and relevant fragments of your documents are found via the vector DB (892); (2) these fragments are passed to the language model as context; (3) the model forms an answer specifically from them, ideally with a source link. It is the foundation for knowledge-base AI assistants (868), internal assistants (884), smart support. Honestly about hallucinations, this is key: RAG is the best practical way to reduce fabrications because the model answers from what was found rather than 'from its head'. But it does NOT eliminate hallucinations fully: the model can misinterpret a fragment, mix sources or make things up if what was found is insufficient. So we add source links (verifiability) and guardrails 'answer only from what was found, else say it was not found', and for the critical we keep human review. Honestly about dependence on data and search: answer quality is determined by two things — how good and current your data is, and how accurately the system found the right fragments (retrieval). If data is bad or search missed — the answer will be bad no matter how good the model. 'Garbage in — garbage out'. Part of the work is putting data in order and tuning search quality. Honestly about complexity: RAG is a system of several components (embeddings, vector DB, retrieval, generation, quality evaluation) requiring setup and testing, not 'plug in a model'. Honestly about cost: embeddings, storage and model calls are paid resources. Honestly about access: your data/documents are needed. An important boundary: this is the RAG architecture; vector DB — 892; embeddings — 894; knowledge-base assistant (ready product) — 868; fine-tuning (a different approach) — 895. Picture this: instead of 'the AI makes things up' — answers from your data with a link, with verifiability. The base price starts from 60,000 ₽ (depends on data and complexity).
Problems we solve
- The AI answers 'from its head' and makes things up, cannot be trusted.
- You need answers strictly from your data, not general ones from the model.
- Answers without source links — they cannot be verified.
- Data exists but the AI does not use it when answering.
What's included in the RAG (Retrieval-Augmented Generation) implementation service
- RAG architecture: retrieval (search) + generation from what was found
- A link of embeddings (894) and the vector DB (892)
- Answers with a source link (verifiability)
- Guardrails: answer only from what was found, else 'not found'
- Tuning search quality (retrieval) and answer evaluation
- Honest boundaries (reduces but does not eliminate hallucinations; depends on data)
- Putting data in order (where needed)
- Handover and review with you
What you get
- The AI answers from your data, not 'from its head'
- Source links — answers are verifiable
- Noticeably fewer hallucinations (not zero — honestly)
- A foundation for knowledge-base assistants (868/884)
How the work goes: steps
- We collect and assess data; set up embeddings and the vector DB
- We build retrieval and generation, add links and guardrails
- We test answer quality, honestly set boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will RAG fully remove hallucinations?
It will noticeably reduce them but not remove fully — that is honest. RAG makes the model answer from fragments found in your data rather than 'from its head', so fabrications become far fewer. But the model can still misinterpret a fragment or make things up if little was found. So we provide source links, guardrails and keep human review for the critical. 'Zero hallucinations' cannot be promised.
What does RAG answer quality depend on?
On two things: the quality and freshness of your data and search accuracy (retrieval). If data is bad or the system found the wrong fragments — the answer will be bad whatever the model ('garbage in — garbage out'). So part of the work is putting data in order and tuning search quality, not just 'plugging in AI'.
RAG or fine-tuning — which to choose for knowledge?
For answers from your facts/documents RAG is usually better: it relies on current data, gives links and updates easily (change a document — answers change). Fine-tuning (895) changes the model's style/behavior but is unreliable as a way to 'inject knowledge' and expensive to update. RAG is often cheaper and more accurate for facts. We will honestly suggest what suits your task.
About the provider
The «RAG (Retrieval-Augmented Generation) implementation» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.