RAG stands for Retrieval-Augmented Generation. When a user asks a question, the system finds relevant passages in selected sources and passes them to a large language model to help it produce an answer.
In a business, these sources might include product manuals, company procedures, service terms or project documentation. An employee asks a question in ordinary language, and the assistant can respond with links to the documents it used. This makes it possible to support staff or customers with information specific to the company.
RAG is worth considering when answers already exist in documents but people spend time finding and connecting them. Its usefulness depends on source quality, retrieval and how the resulting answers are checked.
How RAG turns documents into an answer
A RAG system separates two tasks: preparing information for retrieval and answering an individual question. The AWS explanation of RAG describes this principle. A business generally does not need to fine-tune the generative model simply to supply its own documents as context.
- Prepare the sources. Extract text, remove duplicates and identify document versions. Scanned PDFs may need optical character recognition. Tables must retain the relationship between each value, its column heading and its unit.
- Build a search index. Split documents into meaningful passages while retaining headings and links to the originals. Many systems use vectors, numerical representations of text meaning, to retrieve similar content even when the question and document use different words.
- Retrieve information for the question. Search selects relevant passages. The system must also account for which documents the user is allowed to access. An ambiguous question, such as one without a product model, may need clarification.
- Generate an answer. Give the model the question, selected passages and response instructions. Show the document name, section or link so the user can check the basis of the answer.
Vector search alone does not suit every question. Exact product codes or contract numbers can benefit from keyword search. Hybrid search combines both approaches. Test retrieval methods against questions people actually ask in the business.
What RAG could look like in customer support
Suppose an equipment supplier receives a question about whether a particular unit is suitable for an unheated room. The company has the manufacturer's manual and its own installation guidance, but the employee needs the conditions that apply to that specific model.
The assistant uses the model code to find the operating-temperature passage and installation requirements. It drafts an answer with links to both documents. The employee checks the conditions before sending the response to the customer.
If the customer has not specified the model, the assistant should ask for it. If two manual versions give different temperature limits, it should flag the conflict or refer the question to a specialist. Designing that behaviour is part of implementation; connecting a folder of files is not enough.
RAG helps retrieve and summarise information in this process. Current stock or an individual price needs a separate connection to the sales system. A passage from an outdated catalogue cannot confirm current availability.
Business tasks that can benefit from RAG
Start with a specific information need. Several team tasks can be suitable candidates.
- Internal employee support. Find approved purchasing, leave or equipment-allocation procedures. Link the answer to the current document so the employee can read the complete procedure when needed.
- Technical support. Retrieve manual sections using the product model and problem description. Questions involving hazardous work or unclear faults should go to a specialist under agreed rules.
- Drafting sales proposals. Gather material from service descriptions and approved documents. An employee confirms the specification, price and commitments.
- Finding project information. Retrieve solution descriptions, specifications or documentation from previous work. Make the relevant project and time period clear alongside the information.
RAG does not have to appear as a standalone chatbot. Search and draft answers can sit inside the system an employee already uses. Before choosing an interface, observe where questions arrive and how the answers are used.
How RAG differs from fine-tuning and AI agents
RAG adds retrieved information to the model's input. Fine-tuning changes model parameters using training examples and can help adapt response format or behaviour to a task. Document retrieval is useful when information changes and its source needs to be visible. The two methods can be combined.
An AI agent can select tools and carry out actions. For example, RAG can answer a question using a returns policy, while an agent with the necessary integrations can register a return. The second task needs separately granted permissions, data checks and human approval where required. Google's AI architecture guidance distinguishes information retrieval from action execution.
A small, well-organised manual library may only need conventional search. Uploading a few documents to an approved AI tool may suit a one-off task. Accurate totals across all sales records are better handled through database queries and calculation logic. Retrieval that selects a few passages does not by itself ensure every record is counted.
What affects answer accuracy and data security
RAG can reduce the risk of unsupported answers, but the model can still connect facts incorrectly or rely on an unsuitable passage. The original RAG research demonstrated improvements on the tasks studied, not an infallible system for every business situation. A source link helps users check an answer; it does not prove the document supports every claim.
Resolve outdated and conflicting procedures before introducing an assistant. Assign someone responsibility for each document collection and define how changes reach the search index. For example, Amazon Bedrock knowledge bases process added, changed and deleted documents when the source is synchronised. Deleting a file from its original folder does not confirm its removal from every system layer.
Apply access controls when selecting the information passed to the model. If an employee cannot read an HR document, its passages must not enter their answer context. Document-level access control can use user or group identities and permission metadata. Simply instructing the model not to reveal secrets does not replace this control.
Agree where documents, questions and answers are stored, what data is sent to the model provider, and how logs and backups are handled. Calling an architecture RAG does not define a particular provider's data-use terms.
How to test RAG before a wider rollout
Choose one team and a defined task for a pilot. Collect questions staff genuinely deal with and provide an approved answer and source for each. Include questions whose answers are absent from the documents.
Test outdated document versions, similar product codes, ambiguous wording and users with different permissions. For Lithuanian questions, include the terminology and abbreviations used in the company. Check what happens after a document changes or is deleted.
Write acceptance criteria as concrete tasks. In the equipment-supplier example:
- An answer about a model's operating temperature must match its current manual and identify the relevant section.
- A question about an undocumented use must trigger clarification or referral to a specialist under the agreed workflow.
- After an employee loses access to pricing, a repeated question must reveal neither the price nor passages from the pricing document. Check access to earlier conversations as well.
These criteria make specific failures discussable: a wrong source, an omitted condition or excessive access. Agree them before testing and repeat tests after changing the model, document preparation or search configuration.
RAG evaluation methods distinguish retrieval quality from answer quality. First check whether the necessary passages were retrieved. Then assess whether the response is grounded in them, complete and relevant. Better wording alone will not fix retrieval of the wrong model's manual.
To assess business value, compare the full task time with the previous process. Include checking and correcting the answer and handing work to colleagues. Record wrong answers and justified refusals separately. Costs should include data preparation, integrations, search infrastructure, model usage and maintenance.
What determines the cost of a RAG project
Separate implementation from ongoing costs in a proposal. Initial work includes assessing source preparation, permissions, integrations, the interface and testing. Connecting one tidy manual folder is a different scope from searching documents across departments with different access rights.
Ongoing costs include model requests, search and storage. Document changes also need to reach the index. Ask what usage assumptions underpin the estimate, who maintains the connections and how incorrect answers will be investigated. Compare proposals with the same scope and explicit support responsibilities.
Before commissioning a solution, gather real questions, sample documents and employee roles. Use them to discuss implementing an AI solution and agree how the first working version will be assessed. The initial result should show which questions the system handles reliably and where employee involvement remains necessary.
