What Is Context Engineering? How Enterprises Turn AI Tools Into Business Results

Generative AI is becoming easier to access, but making it useful inside an organisation is becoming a more important leadership problem.

Businesses can now access powerful AI models, copilots, agents and automation platforms without building everything from scratch. Yet access to AI does not automatically create business value. The difficult question is no longer simply, “Which AI tool should we use?” It is, “What does the AI need to know, what should it be allowed to do, and how do we connect it to a business workflow that produces a measurable result?”

This is where Context Engineering for Enterprise AI becomes important.

Why AI tools alone don’t create business results

Most enterprises do not have an AI access problem anymore.

Employees can use ChatGPT, Claude, Gemini and other AI tools to write, summarise, analyse, research and generate ideas. The technology is increasingly available. The harder question is what happens after the AI produces an answer.

An AI tool can improve an individual’s productivity. But business results usually require something more: a business problem, relevant organisational context, a redesigned workflow, clear ownership and a measurable outcome.

That is the gap between using AI and using AI to change how a business works.

The difference between AI usage and business impact

Consider two approaches:

AI-tool mindset	Enterprise AI mindset
Which AI tool should we buy?	Which business problem are we solving?
What prompt should employees use?	What context does the AI need?
How many employees are using AI?	Which workflows have changed?
How impressive is the AI output?	Which business KPI has improved?
Can the model answer the question?	Can the system complete the work reliably?
How quickly can we launch a pilot?	Can we move from pilot to production?

This distinction is central to the LEADxAI approach. The programme is deliberately built around working knowledge and business decisions, rather than teaching leaders how to operate AI tools for their own sake.

What Is Context Engineering for Enterprise AI?

Context Engineering is the practice of designing the information, instructions, knowledge, tools, workflow state and boundaries that an AI system needs to produce useful results for a specific task.

Prompt Engineering focuses primarily on how we communicate with an AI model. Context Engineering takes a broader view. It asks what the AI should know, what information it should retrieve, which tools it should access, what actions it can take, what constraints it must follow and how its performance should be evaluated.

For an enterprise, this distinction matters because business workflows rarely depend on a prompt alone.

A customer-support AI may need access to product documentation and policies. A finance workflow may need structured financial information and approval rules. An internal knowledge assistant may need access to current organisational documents. An AI agent may need permission to interact with specific systems while being prevented from accessing sensitive information.

The model is only one part of that system.

The quality of the context determines how useful the model can be for the business workflow.

The four building blocks of context engineering

A practical enterprise context can be thought of as four layers:

Context layer	What it provides	Enterprise example
Instructions	What the AI should do	“Classify incoming customer complaints”
Knowledge	What the AI should know	Product manuals, policies, customer records
Tools	What the AI can access or use	CRM, database, ticketing system
Rules & boundaries	What the AI can and cannot do	Approval rules, PII restrictions, escalation

These layers work together.

An AI model may be capable of reasoning about a customer complaint, but if it cannot access the latest product policy, it may produce an answer that sounds convincing but is wrong.

Give it the right policy, relevant customer context, access to approved systems and clear escalation rules, and the same underlying model can become part of a much more useful enterprise workflow.

Context engineering vs prompt engineering

Prompt engineering remains useful.

If you are asking an AI model to rewrite a paragraph, analyse a document or create a first draft, a well-structured prompt can make a substantial difference.

But enterprise systems usually involve more than one instruction.

They involve company information, user permissions, historical information, business rules, external systems, tools, examples, workflows and sometimes multiple steps of reasoning.

Anthropic describes context engineering as a progression from prompt engineering towards managing the broader set of information available to a model, including system instructions, tools, MCP, external data and message history.

The distinction can be summarised simply:

Prompt engineering	Context engineering
How should I ask?	What should the AI know and have access to?
Primarily instruction-focused	Environment and information-focused
Often task-specific	Often workflow-specific
Useful for individual productivity	Important for enterprise systems
One part of the solution	Broader system design

The shift is therefore not from prompting to something that makes prompting obsolete.

Prompt engineering becomes one layer within a larger system.

The four levels of enterprise context engineering

Not every enterprise needs an AI agent. Not every problem needs RAG. And very few organisations should jump straight to fine-tuning a model.

A better approach is to understand where your organisation currently sits on the Context Engineering maturity ladder and then choose the simplest technology that solves the problem.

The LEADxAI framework uses four levels:

Level 1 — Prompt Engineering → Level 2 — RAG + MCP → Level 3 — AI Agents → Level 4 — PEFT

Each level increases what AI can know, access, do and adapt to. The goal is not to reach Level 4 as quickly as possible. The goal is to reach the right level for the business problem.

Level	Context Engineering capability	What changes	Business question
Level 1	Prompt Engineering	Tell AI what to do	“How do I communicate with AI effectively?”
Level 2	RAG + MCP	Give AI knowledge and tool access	“How can AI use our company information and systems?”
Level 3	AI Agents	Allow AI to perform multi-step work	“Can AI actually do this workflow for us?”
Level 4	PEFT	Customise model behaviour	“Do we need to customise the model itself?”

The important point is that these levels are not four competing technologies.

They build on one another.

A production enterprise system might eventually combine prompting, retrieval, tools, agents and model customisation. But starting with the most complex architecture is rarely the right first decision.

Level 1: Prompt Engineering — Ask better

The first level is about how you communicate with AI.

At this stage, a person interacts directly with an AI model and provides instructions to produce an output.

For example:

“Analyse these customer complaints, identify the five most common themes and recommend actions for each.”

A better prompt can improve the quality, consistency and usefulness of the response. This is where most employees begin their Generative AI adoption. It is also where many organisations stop.

That is a problem when the business requirement goes beyond individual productivity.

Prompt engineering is valuable for:

• drafting and rewriting
• summarisation
• analysis
• brainstorming
• research
• decision support
• document interpretation
• content creation

But the model may still not know the organisation’s latest policies, customer records, internal procedures or proprietary knowledge.

That creates a natural ceiling.

Level 1 asks: “How do we talk to AI?”

If you are building foundational capability across business teams, this is where enterprise AI training becomes relevant: employees first need to understand what these systems can and cannot reliably do.

The limitation: better instructions cannot compensate for missing enterprise knowledge or missing system access..

Level 2: RAG + MCP — Give AI Knowledge and Tools

The second level changes the environment around the model.

Instead of asking AI to rely primarily on its existing knowledge, the organisation gives it access to relevant external information and, where appropriate, enterprise tools.

This is where Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP) become important.

RAG: Give AI the right knowledge

Imagine an employee asking:

“What is our company’s policy for approving international travel?”

A generic AI model may provide a plausible answer.

A properly designed enterprise AI system can retrieve the relevant company policy and use that information to formulate its response.

The difference is significant.

Prompting:
“Tell me our travel policy.”

Context engineering with RAG:
“Retrieve the current approved travel policy, identify the section relevant to this employee’s situation, and answer using that source.”

This is why RAG is particularly useful for enterprise knowledge.

It can connect AI to:

• internal policies
• product documentation
• standard operating procedures
• knowledge bases
• contracts
• research
• customer information
• internal reports

Sudhanshu’s work already positions Generative AI and RAG as an important enterprise capability, making this a natural internal-link destination from this article.

MCP: Give AI access to tools

Knowledge is only one part of enterprise work.

AI may also need to interact with business systems.

For example:

• CRM
• databases
• calendars
• ticketing systems
• analytics platforms
• enterprise search
• document repositories

MCP provides a standardised way for AI applications to connect with external systems, tools and data sources.

So the distinction is useful:

RAG helps AI find relevant knowledge.
MCP can help AI connect to tools and systems.

The LEADxAI curriculum specifically teaches leaders to evaluate whether their organisation needs RAG, MCP or both, rather than treating either technology as an automatic requirement.

The business question at Level 2 is:

“What information and systems does AI need access to in order to perform this task?”

Level 3: AI Agents — Let AI act

The third level moves from AI that primarily responds to AI that can potentially act.

An AI assistant might answer:

“Here is the process for handling this customer request.”

An AI agent could potentially execute parts of that process:

1. Understand the request.
2. Retrieve the relevant policy.
3. Check customer information.
4. Determine the appropriate action.
5. Use an approved business system.
6. Generate a response.
7. Escalate exceptions to a human.
8. Record the outcome.

That is a fundamentally different enterprise capability.

The LEADxAI curriculum frames the shift simply:

GenAI answers. Agentic AI does.

But autonomy introduces a new requirement: stronger context and stronger boundaries.

An enterprise agent needs to know:

• What is my role?
• What information can I access?
• Which tools can I use?
• What actions am I authorised to perform?
• What requires human approval?
• What should I do when information is missing?
• When should I escalate?
• How will my performance be measured?

This is why agentic AI should not be treated simply as “the next cool AI technology”.

It is a workflow and governance decision.

The LEADxAI AI Virtual Employee Framework takes this further by defining an AI agent through role, KPIs, onboarding, performance review, iteration, code of conduct and a human reporting line.

That framework is particularly useful when evaluating whether an agent should actually be allowed to operate inside a business process.

The business question at Level 3 is:

“Can AI safely perform part of this workflow rather than merely advise a human?”

Level 4: PEFT — Customise model behaviour

The fourth level is about changing the model itself.

PEFT — Parameter-Efficient Fine-Tuning — can be used when an organisation has a strong reason to customise model behaviour rather than solving the problem through prompts, retrieval and workflow design alone.

This is where leaders need to be particularly careful. Fine-tuning sounds like the most sophisticated option. That does not make it the best option.

Consider the decision sequence:

• Can better prompting solve the problem?
• If yes, start there.
• Does the model need access to proprietary or frequently changing knowledge?

Consider RAG.

Does it need to interact with enterprise systems?

Consider tools or MCP.

Does it need to perform a multi-step workflow?

Consider an agent.

Does the organisation genuinely need specialised model behaviour that those approaches cannot provide adequately?

Then investigate PEFT or fine-tuning.

The LEADxAI model explicitly teaches this decision as a progression from Prompt Engineering → RAG → Fine-tuning → Custom Model, with cost, quality and timeline trade-offs considered before choosing the architecture.

For leaders evaluating model strategy, the key question is therefore not:

“Can we fine-tune our model?”

It is:

“What business requirement makes fine-tuning necessary?”

That distinction can save an organisation considerable time, cost and engineering effort.

How the Four Levels Connect to Business Results

The technology only matters when it changes something measurable.

The progression is:

Prompt → Knowledge → Action → Customisation

But the business progression should be:

Productivity → Better decisions → Workflow automation → Strategic advantage

That is why Context Engineering sits between AI tools and business results.

The model is only one component.

The real enterprise system includes the context around it.

And the better that context is designed, the easier it becomes to move from an impressive AI demonstration to a workflow that an organisation can actually trust, measure and scale.

More context does not always mean better AI

There is another misconception worth addressing.

If context improves AI performance, it is tempting to assume that more context must be better.

It isn’t.

A model can be overwhelmed by irrelevant, duplicated, outdated or contradictory information.

Good context is not the largest possible collection of information.

It is the right information at the right time.

Anthropic’s engineering guidance makes a similar point, treating context as a finite resource and recommending the curation of a relatively small, high-signal set of information rather than simply filling the context window.

For an enterprise, that means asking:

• Is this information relevant?
• Is it current?
• Is the source trustworthy?
• Does the user have permission to access it?
• Does the AI actually need it?
• What happens if two sources disagree?
• What should happen when information is missing?

Context engineering is therefore partly an information-management problem.

A practical enterprise context engineering framework

A useful way for business leaders to approach context engineering is to work through seven steps:

Business Problem → Instructions → Knowledge → Tools → Boundaries → AI Workflow → KPI

Step	The leadership question	What needs to be defined
1. Business Problem	What are we actually trying to improve?	Process, pain point, owner and desired outcome
2. Instructions	What should the AI do?	Role, task, output format and operating instructions
3. Knowledge	What does the AI need to know?	Policies, documents, data, history and other trusted sources
4. Tools	What does the AI need to access or use?	CRM, ERP, databases, search, APIs and enterprise applications
5. Boundaries	What must the AI never do?	Permissions, data restrictions, approval rules, escalation and compliance
6. AI Workflow	Where does AI actually fit into the process?	Human-in-the-loop, automation, agentic steps and hand-offs
7. KPI	How will we know it worked?	Time, cost, quality, revenue, adoption, accuracy or risk metrics

1. Start with the business problem

Do not begin with:

“Where can we use an AI agent?”

Begin with:

“Which workflow is expensive, slow, repetitive, error-prone or difficult to scale?”

For example, instead of saying “We need an AI chatbot for customer service,” define the problem as:

“Customer-service teams spend 35% of their time searching across product documentation and policy documents before responding to routine queries.”

Now there is something measurable to solve.

2. Define the instructions

Next, define the AI’s role.

The system needs to know:

• what its job is
• what a good output looks like
• what process it follows
• what tone or format it should use
• what to do when information is missing

This is where Prompt Engineering sits within the broader Context Engineering model.

3. Identify the knowledge

Ask:

What information must the AI have to perform this job correctly?

This could include:

• internal policies
• product documentation
• SOPs
• customer records
• research
• contracts
• knowledge bases

For enterprise knowledge, RAG can provide the retrieval layer that brings relevant information into the model’s context when it is needed.

But the objective isn’t to give the AI all company information.

It is to give it the right information for the task.

4. Identify the right tools

Knowledge alone isn’t enough when the AI needs to act.

Ask:

What systems does the AI need to access to complete the workflow?

For example:

Customer-support agent

→ Knowledge base
→ CRM
→ Ticketing system
→ Order database
→ Escalation workflow

This is where tool use and technologies such as MCP can become relevant.

5. Establish boundaries

This is one of the most important—and most frequently neglected—parts of enterprise context engineering.

Define:

What can the AI do?

And equally:

What can it not do?

For example:

AI can	AI cannot
Retrieve customer information	Expose another customer’s data
Draft a response	Approve a refund above ₹50,000
Create a support ticket	Delete customer records
Recommend an action	Make a legally binding decision
Escalate an exception	Override security controls

The goal is not maximum autonomy.

The goal is appropriate autonomy.

6. Design the actual AI workflow

Now connect everything together:

Input → Context → AI reasoning → Action → Human review → Outcome

At this point you can decide whether the workflow needs:

• a conventional automation
• an LLM
• RAG
• an AI agent
• tool calling
• MCP
• PEFT/fine-tuning
• or simply better software

This is also where the AI Decision Filter becomes useful: not every business problem requires AI.

7. Define the KPI before deployment

Finally:

What measurable business result will tell us this works?

Don’t stop at:

“Employees like the tool.”

Measure the workflow.

Business objective	Possible KPI
Faster service	Resolution time
Lower cost	Cost per transaction
Higher productivity	Hours saved
Better quality	Error/rework rate
Better sales	Conversion rate
Better operations	Throughput
Safer AI	Escalation/error rate
Adoption	Active usage in target workflow

This is where Context Engineering connects to business ROI.

A simple example: from AI chatbot to AI workflow

Consider customer support.

A basic AI implementation might look like this:

Customer question → AI generates answer

That may work for simple questions.

A context-engineered system could look very different:

Customer question → identify intent → retrieve approved product/policy information → check relevant customer context → apply response rules → use approved tools if required → respond or escalate → record outcome

The difference is important:

AI chatbot	AI workflow
Answers a question	Completes a business process
Uses available information	Retrieves the right business context
Mostly conversational	Connected to business systems
Produces an output	Can take permitted actions
Success = good answer	Success = measurable business outcome
Human completes the process	AI + human work together

This is the shift from using AI as a tool to engineering AI into the workflow.

Where RAG fits into context engineering

RAG sits at Level 2 of the Context Engineering stack.

Its job is to give an AI system access to relevant external knowledge at the time it needs it.

The simplest way to explain the relationship is:

Prompt Engineering tells AI what to do. RAG gives it the knowledge it needs to do it.

In the LEADxAI framework, the progression is:

Level 1 — Prompt Engineering → Level 2 — RAG + MCP → Level 3 — AI Agents → Level 4 — PEFT

What RAG actually adds

Without RAG:

User → Prompt → LLM → Answer

The model primarily works from its existing capabilities and whatever information is included in the conversation.

With RAG:

User → Query → Retrieve relevant enterprise information → LLM → Answer

That retrieved context might come from:

• internal policies
• product documentation
• SOPs
• contracts
• knowledge bases
• research documents
• customer information
• enterprise databases

The important point is that RAG doesn’t replace the model. It supplies additional context to the model.

RAG is one layer, not the whole Context Engineering system

This distinction is important for Sudhanshu’s positioning.

A complete enterprise Context Engineering system can be thought of as:

Layer	Question it answers
Prompt Engineering	What should AI do?
RAG	What knowledge should AI have?
MCP / Tools	What systems can AI access?
AI Agents	What multi-step work can AI perform?
PEFT	When does the model itself need customisation?
Governance	What is AI allowed to do?

So if an employee asks an AI system:

“Can this customer receive a refund?”

RAG might retrieve the latest refund policy and relevant product documentation.

But RAG alone does not necessarily:

• check the customer’s account,
• calculate the refund,
• initiate the transaction,
• decide whether approval is required,
• escalate an exception.

Those require tools, workflow logic, agents and governance.

That’s why the LEADxAI material frames RAG + MCP together at Level 2: RAG brings knowledge, while MCP connects AI applications with external tools and systems.

Where MCP fits into context engineering

Model Context Protocol (MCP) sits in the “tools and systems” layer of Context Engineering.

A simple way to understand the relationship is:

Prompt engineering tells AI what to do. RAG gives it knowledge. MCP helps it connect to tools and systems. AI agents use those capabilities to take action.

In the LEADxAI framework, this makes MCP part of Level 2 — RAG + MCP, between basic prompt engineering and autonomous AI agents.

If RAG helps an AI system access relevant knowledge, MCP can provide a standardised way for AI applications to connect with external systems and tools. The official MCP documentation describes connections to data sources, tools and workflows, with examples including calendars, databases and other applications.

For an enterprise leader, the practical question is not:

“Should we use MCP because it is new?”

It is:

“Does our AI system need reliable access to external tools or systems, and what is the safest architecture for providing it?”

That is the level at which business leaders should evaluate emerging AI infrastructure.

Where AI agents fit

AI agents sit at Level 3 of the Context Engineering model.

The progression is:

Prompt Engineering → RAG + MCP → AI Agents → PEFT

The important distinction is that an AI agent does not simply generate an answer. It can use the context available to it to reason through a task, use tools, take actions and respond to changing conditions.

For an enterprise, that means the question changes from:

“Can AI answer this?”

to:

“Can AI reliably complete this workflow?”

What an AI agent needs

An enterprise agent typically needs four things:

Component	What it provides
Instructions	Defines the agent’s role and objective
Knowledge	Gives it relevant company information, often through RAG
Tools	Allows it to interact with enterprise systems
Rules & boundaries	Defines permissions, escalation and actions it must not take

This is why agents belong inside Context Engineering rather than being treated as a completely separate AI technology

When should an enterprise use prompting, RAG, agents or PEFT?

If the problem is…	Use…	Why
“The AI knows enough, but we need better instructions.”	Prompting	Change how you communicate the task.
“The AI needs access to our private/current knowledge.”	RAG	Give the model relevant external information at inference time.
“The AI needs to perform a multi-step task or take actions.”	Agents	Give AI tools, decision logic and the ability to execute workflows.
“The model itself needs specialised behaviour.”	PEFT / fine-tuning	Adapt the model’s behaviour rather than simply supplying more context.

This is essentially the four-level Context Engineering ladder in the LEADxAI material: Prompt Engineering → RAG & MCP → AI Agents → PEFT.

1. Use prompting when the task is primarily communication

Choose prompting when:

• the model already has the necessary information;
• you need a better structure, tone or format;
• the task is relatively self-contained;
• there is no need for the AI to access enterprise systems.

Example:
“Analyse this quarterly report and produce a one-page executive summary.”

You don’t need RAG or an agent simply to improve the instruction.

Leader’s question:

Can better instructions solve the problem?

If yes, start with prompting.

2. Use RAG when the AI needs enterprise knowledge

Choose RAG when the model needs information that is:

• private;
• proprietary;
• frequently changing;
• too specific to reliably exist in the base model.

Examples:

• HR policy assistant
• product documentation assistant
• internal compliance assistant
• customer-support knowledge system
• enterprise research assistant

The key distinction is:

Prompting tells the model what to do. RAG gives it the information it needs to do it.

The LEADxAI blueprint explicitly positions RAG as the level where organisations bring their own data and knowledge into the AI workflow.

Leader’s question:

Does the AI need access to information it doesn’t already have?

If yes, investigate RAG before fine-tuning.

3. Use agents when the AI needs to act

Choose agents when the task is not simply:

Question → Answer

but rather:

Goal → Plan → Retrieve information → Use tools → Take actions → Check result → Continue/escalate

Example: customer-support agent

Instead of:

“Write a response to this customer.”

An agent might:

1. Read the customer request.
2. Retrieve the relevant policy.
3. Check the customer’s account.
4. Determine eligibility.
5. Create a service ticket.
6. Respond to the customer.
7. Escalate exceptions.

This is where tools, permissions, guardrails and human accountability become critical.

The LEADxAI framework describes agents as the level where AI can perform autonomous workflows rather than merely provide answers.

Leader’s question:

Does AI need to take actions across multiple steps or systems?

If yes, an agent may be appropriate.

4. Use PEFT when the model itself needs customisation

PEFT—Parameter-Efficient Fine-Tuning—is appropriate when the organisation needs to change or specialise model behaviour rather than simply provide additional information.

Think of it this way:

RAG: “Give the model the information.”

PEFT: “Adapt the model to behave differently.”

Potential situations include specialised:

• language/style behaviour;
• classification;
• domain-specific patterns;
• structured outputs;
• task behaviour.

But this should usually be a later decision, not the first technology an enterprise reaches for.

If better prompting or relevant retrieved context solves the problem, there may be little reason to customise the model itself.

The LEADxAI material frames PEFT around the leader’s question: “When should we build our own AI model?”

Leader’s question:

Is the problem really the model’s behaviour, rather than missing knowledge or missing tools?

If yes, investigate PEFT/fine-tuning.

Why enterprise AI pilots often stop before business results

Many organisations reach the pilot stage.

Fewer reach production.

And fewer still create measurable, sustained business impact.

The problem can occur at several points:

AI idea → use case → data → architecture → governance → adoption → KPI → production

Break the chain anywhere and the project becomes harder to scale.

A technically impressive proof of concept may still fail because:

• the underlying data is poor
• security blocks deployment
• the workflow was never redesigned
• employees do not adopt it
• ownership is unclear
• the economics do not work
• governance arrives too late
• nobody defined the production KPI

This is why enterprise AI leadership is increasingly about decisions rather than simply technology exposure.

Seven questions every business leader should ask

Before approving an enterprise GenAI project, ask:

1. What business problem are we solving?
2. What information does the AI need?
3. What information should it not have access to?
4. Which tools or systems does it need to use?
5. What actions is it allowed to take?
6. When must a human take over?
7. Which KPI will prove that the system is working?

If those questions cannot be answered, buying another AI subscription is unlikely to solve the underlying problem.

How to measure context engineering ROI

The wrong metric is:

“How many employees are using AI?”

Usage is useful, but it is not the final business outcome.

A better measurement framework looks at the workflow.

Area	Possible KPI
Productivity	Hours saved per workflow
Speed	Cycle-time reduction
Quality	Error or rework reduction
Customer service	Resolution time
Sales	Conversion or response time
Finance	Processing cost or error rate
Operations	Throughput
AI reliability	Escalation/error rate
Adoption	Active usage within the target workflow
Economics	Cost per completed transaction

The question I would ask is simple:

What is measurably better because we introduced AI?

One workflow that materially improves customer response time can be more valuable than twenty internal AI experiments.

One finance process that reduces error and cycle time can be more valuable than an organisation-wide prompt competition.

One production AI agent with clear boundaries and measurable economics can be more valuable than dozens of autonomous-agent prototypes.

What business leaders should remember

The enterprise AI conversation is moving beyond:

“Which AI tool should we buy?”

It is moving towards:

“How do we design an AI system that can reliably operate inside our business?”

That requires a broader view.

The model matters.

The prompt matters.

But so do the knowledge sources, tools, permissions, workflow, governance and measurement system around the model.

That is the role of context engineering.

The future of enterprise AI is better context

AI models will continue to improve.

Their reasoning capabilities will improve. Their context windows will change. Tool use will become more capable. Agentic systems will become easier to build.

That does not remove the enterprise context problem.

It makes it more important.

An organisation still needs to decide:

• what information matters
• what the AI can access
• what it can do
• what it cannot do
• who owns the outcome
• how the system is evaluated
• whether the economics justify deployment

The competitive advantage will therefore not come simply from having access to the newest model.

It will increasingly come from knowing how to connect that model to the right business context.

The simplest way to remember it is:

Better context → Better AI → Better workflows → Better business results.

Frequently Asked Questions