Context layers, enterprise search, RAG and custom instructions

Enterprise search helps people find information. RAG is an approach for retrieving information to inform a model’s response. Custom instructions provide recurring directions in an AI tool. A company context layer adds an operating model for selecting current knowledge, approving guidance and delivering appropriate context. Products can combine several of these capabilities.

Start with the job you need done

If colleagues cannot find a document, better search may solve the immediate problem. If an assistant needs relevant passages from a document collection, retrieval may be the central technical requirement. If a small team wants a shared writing preference, a short set of instructions may be sufficient.

The problem changes when different tools need current company knowledge, source conflicts need accountable decisions and guidance varies by audience. At that point, evaluating only retrieval quality or prompt length misses part of the work. You also need to understand how sources, owners, approvals, access and updates fit together.

Enterprise search: finding information across systems

Search makes a collection easier to navigate. For an employee asking where the handover checklist lives, a relevant result and a usable source link may be the whole answer. Search quality depends on factors such as coverage, indexing, relevance and access controls.

Modern enterprise-search products can also supply answers and context to AI tools. Do not assume the category stops at a search box. When evaluating a product, test what it actually offers: source freshness, permissions, verification, delivery routes and the way conflicting material is handled. Category names are less useful than observed behaviour on your company’s tasks.

RAG: retrieving evidence for a response

Retrieval-augmented generation describes a pattern in which relevant information is retrieved and supplied to a model as it produces an answer. It can help an assistant use material outside its training knowledge and provide evidence for specific statements. It is an implementation approach rather than a complete company operating model.

A retrieval system still needs decisions about which sources are included, who may receive them and how changes are processed. Finding a highly relevant paragraph does not establish that the paragraph is current policy. For a proposal task, both an old offer and a current offer may be relevant; someone still needs to establish which source governs today’s work.

Custom instructions: useful directions with a maintenance cost

A short instruction can be a sensible way to express stable preferences or explain when an assistant should use an available tool. It is easy to inspect and can work well for a bounded experiment. Problems appear when instructions become the only copy of changing company facts or accumulate several competing rules without ownership.

Imagine the same commercial terms pasted into two assistants, three team templates and a shared prompt document. A policy change now requires updating every copy and checking that employees use the replacements. Keep enduring tool-use directions concise, and maintain changing knowledge and consequential guidance through an explicit source and ownership process.

A context layer: coordinating the company decisions around delivery

The useful distinction is the responsibility a system takes for company context. Can an owner identify the authoritative source for a topic? Can ordinary facts remain reference material while consequential rules receive approval? Can the organisation explain which audiences receive what, and test an update across its supported AI tools?

A context layer may use search, retrieval and protocols such as MCP underneath. These approaches are complementary. Steer focuses on selected live sources, owner-approved guidance and audience-aware delivery into existing AI tools. That is the combination to evaluate; it is not a claim that other products lack governance or cross-tool capabilities.

Approach	Primary question	Check before choosing
Enterprise search	Where is the information?	Coverage, relevance and access
RAG	What evidence should inform this response?	Retrieval quality, source currency and attribution
Custom instructions	What recurring directions should this tool receive?	Ownership, duplication and update process
Company context layer	Which company context applies to this person and task?	Authority, approvals, audience and delivery evidence

Choose with a task-based evaluation

Use the same realistic task to evaluate the alternatives. Include a current source, an older conflicting source, one applicable company rule and a restricted document that should remain unavailable. Ask ordinary employee questions and inspect the answer, citations and denied-information behaviour.

Then change a source and repeat the test. Record administrator effort, employee friction and the quality of the resulting work. A product with fewer connectors may be a better fit if it handles the sources and decisions your company needs reliably. Equally, existing search or native AI-tool features may already cover a narrow use case. Select the smallest approach that meets the actual requirements.