How to Give AI Useful Context About Your Business

Employees asking an AI assistant to draft customer replies, summarize sales notes, or explain a product must give it a clear request plus the business facts and rules it needs to answer well.

Business context is information about your company and its customers, the task, and what the AI should or should not do. Use current information that fits the task. Tell the assistant which sources to trust and what to do when information is missing or conflicting. It helps the assistant answer in your business’s setting, but it does not guarantee that the answer is correct.

Define the task before adding company information

Give the assistant one specific task. “Help with sales” leaves the assistant to guess what work it should do. “Summarize call notes for an account manager and flag questions the notes do not answer” gives it a defined task and a clear boundary.

For each task, decide who will use the output, who will read it, and what the output must include. A support assistant that drafts replies needs different information from one that summarizes internal reports. NIST recommends documenting the AI’s purpose, users, assumptions, limits, and possible effects, not just the information it receives. The NIST AI Risk Management Framework Playbook offers a structure for defining that setting.

Separate behavior rules from business facts

Instructions tell the assistant how to do a task. Reference documents give it facts to use. For example, an instruction might say, “Use approved product information and flag unanswered questions.” A product guide supplies the current features and limitations.

Keeping instructions separate from reference documents makes updates easier. When a feature changes, the person responsible for product information can update the guide without changing the assistant’s general rules. OpenAI’s guidance for custom GPT instructions and knowledge files also distinguishes between instructions and reference material, and recommends clear, text-focused files.

For each task, tell the assistant who the output is for, what it should include, which sources to trust, and what limits to follow. Explain how it should handle missing or conflicting facts. For instance, tell it to flag a conflict between two policies instead of choosing one silently. If it should ask a follow-up question rather than guess, state that explicitly.

Choose focused, current references

Give the assistant the smallest set of documents that answers the questions the task is likely to raise. A support-reply assistant might need current product documentation, approved customer policies, and a glossary for internal terms. It does not need every company file.

Label documents with useful details such as their owner, effective date, and version. Identify which document takes precedence when two sources disagree. Keep changing facts, such as product availability or service policies, in documents someone can update. Put stable information, such as the company’s audience and terminology, in a short business profile.

Some AI tools search company documents to answer users’ questions. This method is called retrieval-augmented generation, or RAG. The tool finds relevant passages in a document collection and gives them to the AI while it forms an answer. Research on RAG found benefits on specific knowledge-based benchmarks, but those results do not establish that every business document search will improve answers. The search can miss a relevant passage or return conflicting information, and the AI can misread what it finds. Ask the assistant to identify the document supporting an important claim, then check that document yourself.

More text does not automatically produce a better answer. In a 2023 study of specific, historical AI models, answers often became less reliable when relevant information sat in the middle of long inputs. The study does not establish the same effect for every current product or business task, but it supports a practical rule: give the assistant relevant material and test whether it uses it, rather than assuming it will find every useful detail in a large collection. See Lost in the Middle for the study’s findings and limits.

Test how the assistant uses the context

Before relying on an AI assistant for recurring work, try questions that reflect actual use. Include questions the documents answer directly and questions they cannot answer. Also ask about cases where two sources disagree. Also test whether the assistant invents a policy, feature, or promise when the references do not support one.

Check whether the assistant uses the right source, follows the stated limits, and gives the reader enough information to act. If it makes an unsupported claim, revise the instructions or reference documents and test it again. A citation or document name helps a reviewer check the source, but the reviewer still needs to confirm that the answer describes it correctly.

Protect information and limit authority

Decide what information the AI needs before sharing company documents. Leave out passwords, API keys, and other secrets. Share only personal or confidential information needed for the task, and check the specific AI product’s data handling, retention, and access settings. For example, OpenAI says it does not use data from certain named business products and its API platform to train models by default. This applies only to those offerings. Check the terms for the product and configuration you use in OpenAI’s business data policy.

A prompt cannot enforce access permissions or prevent every unsafe action. Prompt injection is an attempt to manipulate an AI with instructions hidden in user input or documents the AI reads. The OWASP guidance on prompt injection explains that retrieval and other techniques do not fully eliminate this risk. Set permissions and action limits in the software that connects the AI to company data or tools. Have a person review decisions with significant consequences.

What I'm building

Delegate tasks. Get software.

Give Vroni a GitHub issue, bug report, spec, or rough idea. It reads the repo, plans the change, writes code, runs checks, and works toward a review-ready pull request.

Take a look at vroni.com