Retrieval Mechanisms for AI Context

Imagine a platform that answers customer questions about a company's products. The model at its center can produce fluent, well-formed text. But fluency is not the same as knowing the right answer. A customer asks whether a particular product can be returned after a certain number of days. The model might write a confident, polished reply, yet nothing in its own knowledge guarantees that the reply matches the company's current policy.

The rest of this article is about the work that puts that information in front of the model. The source frames this as the first step in growing a platform: early on, the system is extended with mechanisms that let it assemble the context each query needs. The mechanisms, the role of tools, and the differences between model API providers are all parts of the same job.

The phrase context refers to everything the model receives for a given query. The raw store of possible information a platform holds is not useful on its own. It must be selected and arranged so that the model receives what it needs for the current query. The model cannot use information it never receives, so the quality of what is gathered sets a ceiling on the quality of what comes back.

The source offers an analogy. In classical machine learning, a practitioner shapes raw data into the inputs a model can use well; this is feature engineering. Context construction plays a similar role for a foundation model. Just as raw data is reshaped into features before a traditional model can learn from it, the stored information is reshaped into context before a foundation model can respond to a query.

Read the pipeline below from left to right. A user's query and the platform's stored sources enter the retrieval mechanisms. Each mechanism contributes material to a single constructed context. That context is what the model actually reads, and the output is produced from it. The model never sees the retrieval mechanisms directly; it sees only the context they assembled.

Notice the position of the model in this flow. It is the last stage before the output, and it has no direct access to the source material. Its only window onto the platform's information is the constructed context. This is why the choice and quality of retrieval determine so much of what the model can say.

The source names three retrieval mechanisms for building context: text retrieval, image retrieval, and tabular data retrieval. Each one gathers a different kind of material and hands it to the model as part of the context for a query. They are not competing alternatives for one task. A single platform may use more than one of them, depending on what kinds of information a given query requires.

The clearest way to see why context matters is to hold the question constant and change only the information present. In the first version, the model receives the customer's question with nothing else. In the second, the same question arrives together with the relevant policy and the product record. The model is the same in both cases; only its input differs.

The left path shows what happens when the context lacks the policy. The model still produces fluent text, but nothing in that context ties the answer to the company's actual rules. The right path shows the same model receiving the policy record as part of its context. The change in output comes entirely from the change in what was constructed, which is the central claim of this article.

Retrieval gathers information from sources the platform already holds. The source describes a second route: context can also be augmented using tools. A tool allows the model to automatically gather information through APIs. The examples given are web search, news, weather, and events. Instead of the platform preparing every fact in advance, the model can reach out for a fact it needs at the moment a query arrives.

Follow the numbered messages. The model requests a fact, the tool calls an external API, and the API returns data. That returned data becomes part of the context, just as retrieved text or tables do. The model then uses the enriched context to produce the final answer, which goes back to the user. The final answer comes from the model, not from the API. The API supplies a fact; the model turns that fact, together with everything else in its context, into a response.

Major model API providers, such as OpenAI, Claude, and Gemini, allow users to upload files and allow their models to use tools. Support for context construction is therefore widespread. The source also notes that the providers differ in how much context construction support they offer. Those differences appear in the types and number of documents that can be uploaded, the retrieval algorithms and configurations used (such as chunk size), and the tools and execution modes available.