Context is the information available to an AI model when it interprets a request and generates a response.

It can include instructions, conversation history, uploaded information, retrieved content, tool results, user details, and workflow state.

Context strongly influences what the model understands and how it responds.

What Is Context in AI?

A model does not evaluate a user message in isolation.

It can also receive surrounding information that helps define:

The task
The role
The user
The conversation
The relevant business information
The current workflow state

This surrounding information is context.

For example, the same message:

"Can I cancel it?"

means very little on its own.

With context, the model may know:

The user is discussing an appointment
The appointment is tomorrow
The business has a 24-hour cancellation policy

The answer becomes much more precise.

What Can Be Included in Context?

System Instructions

These define the model's role and behavior.

User Messages

The current request is part of the context.

Conversation History

Previous messages provide continuity.

Uploaded Information

Policies, guides, product information, or other source material can be included.

Retrieved Information

Search results can be inserted into context.

Tool Results

Data returned by APIs, calculations, or other tools can be included.

Workflow State

The model may receive information about what has already happened in the process.

Context vs. Context Window

Context Window describes the amount of information a model can consider at one time.

Context describes the information itself.

A useful distinction is:

Context: what the model sees.

Context window: how much it can see at once.

Context vs. Memory

Agent Memory stores information so it can be reused later.

Context is the information currently available to the model.

Memory can be retrieved and placed into context.

This means memory and context are related but not interchangeable.

Context vs. Knowledge

A model may have general knowledge from training.

Context is information supplied for the current interaction.

For business workflows, context is especially important because it can contain information that is:

Private
Current
User-specific
Business-specific
Temporary

Context and Grounding

Grounding uses relevant source information to support an answer.

That source information must ultimately become available to the model as context.

For example:

A search system retrieves a policy.
The policy is placed into context.
The model uses it to generate the answer.

Grounding therefore depends on effective context construction.

Context and Search

Search can be used to select which information should enter the context.

This is useful when the full source is too large.

A search system may retrieve the top relevant passages and supply only those.

For smaller sources, direct context may be simpler.

Context Quality

More context does not always mean better context.

Poor context can include:

Irrelevant information
Conflicting sources
Outdated details
Duplicates
Excessive conversation history
Ambiguous instructions

Good context is focused, relevant, and authoritative.

Context in Multi-Step AI Workflows

In a multi-step workflow, context may change over time.

For example:

The user provides a request.
The system retrieves data.
A tool returns a result.
The workflow collects another detail.
The model generates the final answer.

Each step may add or remove context.

This is one reason AI orchestration matters.

Context for AI Agents

An AI agent uses context to determine:

What the user wants
What has already happened
Which tools are available
What information is relevant
Which action should happen next

Without sufficient context, even a strong model can make poor decisions.

Direct Context vs. Retrieval

Direct context is useful when the relevant source fits within the available context window.

Retrieval is useful when:

The collection is much larger
Only part of the source is relevant
Search precision matters
Information is distributed across many files

Many production systems use both approaches.

Context in AskHandle

AskHandle can provide context to AI workflows in several ways.

AI Answer can use uploaded information directly in context, including up to 200,000 words.

Document Search and Data Search can retrieve relevant information from larger collections and pass the strongest results into the workflow.

Other nodes and tools can also contribute conversation state, user input, search results, or tool outputs.

This allows the amount and type of context to match the task.

Designing Better Context

A strong context design should ask:

What information does the model actually need?
Which source is authoritative?
What can be omitted?
Does older context still matter?
Are there conflicts?
Should the source be retrieved or included directly?
Which tool results should remain available?
What information is sensitive?

The goal is not maximum context. It is useful context.