How to stop repeating yourself and start getting consistently brilliant results from AI

We’ve all been there. You’re having a perfectly productive conversation with ChatGPT about your marketing strategy when suddenly you need to pop out for a coffee. When you return, you start a fresh chat and find yourself typing the same tedious preamble: “I’m the marketing director at a B2B SaaS company, we focus on mid-market clients, our tone is professional but approachable, we’ve got a £50k budget for Q2…”

By the time you’ve finished recreating the context, you’ve forgotten what you actually wanted to ask.

This isn’t just mildly annoying; it’s a symptom of a deeper problem. Most of us treat every AI interaction as starting from a blank slate, as if the assistant has just been wheeled out of the factory with no memory of who we are or how we work. We end up spending more time explaining ourselves than actually getting things done.

The hidden tragedy? We don’t realise that context has structure. Not all context is created equal, and treating it as one homogeneous blob is like trying to pack for a holiday by stuffing everything into a carrier bag. It works, technically, but it’s hardly elegant.

The solution lies in understanding that context operates on three distinct layers: fixed context (what never changes about how you work), session context (what’s consistent for your current project), and variable context (what’s specific to today’s particular request). Once you grasp this framework and learn to separate these layers properly, AI interactions transform from repetitive, inconsistent exchanges into efficient, predictable workflows.

And the best part? You can implement this immediately using nothing more sophisticated than markdown templates. No special software, no complex systems, just a bit of structured thinking that will save you hours of repetitive typing and deliver consistently better results.

The Context Problem We All Face

The First Date Syndrome

Let’s be honest: the way most people use AI tools is remarkably inefficient. We treat every conversation like a first date, nervously explaining our entire background before getting to the interesting bits:

“I’m a software architect, I prefer clean code…”

“I work in TypeScript, I care about performance…”

“I need documentation, I follow SOLID principles…”

Sound familiar?

This repetitive context-setting isn’t just tedious; it’s cognitively exhausting. Every time you start a new chat, you’re making dozens of micro-decisions about what background information to include. Should you mention your company size? Your technical constraints? Your communication preferences? The result is decision fatigue before you’ve even asked your real question.

The Inconsistency Problem

The inconsistency problem is even worse. Because you’re reconstructing context from memory each time, you inevitably vary the details. One day you mention you’re “senior developer”, the next you say “tech lead”. One conversation includes your preference for functional programming, another doesn’t. These slight variations lead to subtly different AI responses, making it impossible to build reliable, repeatable workflows.

Imagine five people on a product team, each explaining their role and project context slightly differently:

You end up with a cacophony of inconsistent outputs that feel like they’re talking about different products entirely.

Current Workarounds (And Why They Fail)

Current workarounds are charmingly primitive. Some people maintain sprawling notes documents with context they copy and paste. Others screenshot previous conversations to reference later. The more organised among us create informal templates in our heads, but they’re never quite the same twice. These approaches are like trying to manage a library with Post-it notes; they work until they don’t, and they certainly don’t scale.

The real kicker? This inefficiency isn’t just about the time wasted typing. When you’re constantly focused on reconstructing context, you’re not thinking clearly about the actual problem you’re trying to solve. You’re in administrative mode when you should be in creative mode. It’s like spending so much time setting up your easel that you forget what you wanted to paint.

The Three-Layer Context Framework

The breakthrough insight is recognising that context isn’t a monolith. It has natural layers, each with different characteristics and lifecycles. Understanding these layers transforms how you approach AI interactions, rather like learning that ingredients have different cooking times transforms how you approach making a proper curry.

Fixed Context: Your Professional DNA

Fixed Context represents your consistent standards and preferences. This is who you are professionally and how you like to work. It’s like training a good assistant who learns your quirks and anticipates your needs - once established, your AI interactions become predictable and aligned with your working style.

The power of fixed context lies in eliminating repetitive decision-making. Instead of reconsidering your communication preferences every time, you establish them once and move on to more interesting problems. A marketing director might establish: “I write for B2B decision-makers, use data to support claims, and always include clear next steps.” With that fixed, they can focus on crafting compelling messages rather than remembering their house style.

Key Components:

• Professional role and expertise level
• Communication style and tone preferences
• Quality standards and non-negotiable requirements
• Preferred frameworks, methodologies, and decision-making processes

Why It Transforms Your Workflow:

• Eliminates the need to re-specify preferences in every interaction
• Creates consistency across all AI-generated outputs
• Reduces cognitive load by removing repetitive context-setting

Example:

“I’m a senior software architect who prioritises maintainability over cleverness, requires all recommendations to address security and performance, and prefers the Problem → Constraints → Options → Recommendation format.”

Session Context: The Project Bridge

Session Context captures the temporary but crucial parameters of your current work. This bridges your permanent working style with the specific challenges and constraints of today’s project. It’s the context that changes every few weeks or months, staying consistent long enough to avoid repetition but temporary enough to remain relevant.

The beauty of session context is preventing the awkward context drift that happens in long conversations. As discussions evolve, it’s easy to lose track of the original project parameters. By explicitly defining session context, you maintain focus and ensure recommendations remain relevant to your actual situation.

Key Components:

• Current project scope, goals, and success metrics
• Team composition, stakeholder relationships, and decision-makers
• Budget limitations, timeline constraints, and resource availability
• Target audience characteristics and market context

Why It Eliminates Repetition:

• Provides project-specific relevance without re-explaining background
• Maintains consistent assumptions across multiple related conversations
• Prevents recommendations that ignore your current constraints

Example:

“Building a customer data platform for mid-market SaaS companies, 3 engineers plus 1 junior developer, 6-month timeline, £30k infrastructure budget, must integrate with existing PostgreSQL and AWS stack.”

Variable Context: Where the Real Work Happens

Variable Context contains the immediate, specific information that makes each interaction unique. This is your actual question, today’s particular data, the urgent deadline, or the unexpected constraint that just emerged. It’s the reason you’re starting this conversation in the first place.

The crucial insight is that you should spend most of your mental energy on variable context because it’s where the real work happens. Fixed and session context are scaffolding; variable context is the actual building. When you’ve properly separated these layers, you can focus on crafting precise questions and providing relevant data rather than reconstructing your entire working context from scratch.

Key Components:

• The specific question or decision you need help with
• Fresh data, metrics, or information relevant to this request
• Immediate deadlines or time-sensitive constraints
• Unique circumstances that apply only to this interaction

Why It Deserves Your Focus:

• Contains the only information that’s truly new each time
• Drives the specific value you’ll get from this conversation
• Represents the actual problem-solving work rather than administrative setup

Example:

“Should we prioritise mobile app or web dashboard for Q3 sprint? This week’s analytics show 60% mobile usage, but web users convert 2x better. Need decision by Friday for sprint planning.”

How the Layers Work Together

The layers interact in powerful ways. Fixed context ensures consistency across all your AI interactions. Session context provides relevance for your current project. Variable context drives the specific conversation. Together, they create a framework that’s both consistent and flexible, rather like a well-designed API that’s stable enough to rely on but expressive enough to handle diverse use cases.

Markdown Templates: The Practical Solution

Why Markdown Wins

Markdown emerges as the perfect tool for managing these context layers, not through any grand design but through a series of happy accidents.

• Universal: Every developer and most knowledge workers already know it
• Human-readable: You can scan it quickly to verify it’s correct
• Structured: Headings and sections create clear hierarchies
• Version-controllable: You can improve templates over time and share them with colleagues

Most importantly, markdown doesn’t require special software. You can create these templates in any text editor, store them in your notes app, or save them as snippets in your code editor. They’re as portable as a well-worn notebook and just as reliable.

Template Anatomy

The anatomy of a good context template follows a predictable pattern. Headers separate the three context layers clearly. Fill-in-the-blank sections (marked with square brackets or other clear indicators) show exactly what needs to be customised for each use. Comments or examples provide guidance for future use. The template becomes a form you fill out rather than a document you write from scratch.

Let’s examine three detailed examples that demonstrate how this works in practice.

Example 1: Email Generation Template

# EMAIL GENERATION TEMPLATE

## FIXED CONTEXT

**Role:** Marketing Director with 10+ years B2B experience
**Communication Style:** Professional but conversational, always include clear next steps
**Quality Standards:** Subject line must clearly indicate required action
**Format Preference:** Maximum 3 paragraphs, bullet points for multiple items

## SESSION CONTEXT

**Current Campaign:** Q2 Product Launch - CRM Integration Feature
**Target Audience:** Mid-market operations managers who attended our webinar series
**Campaign Goal:** Convert qualified leads to sales meetings
**Success Metric:** 15% meeting booking rate from follow-up emails
**Key Messaging:** Integration reduces manual data entry by 75%

## VARIABLE CONTEXT

**Recipient:** [Name and company]
**Specific Objective:** [Meeting request/demo scheduling/follow-up question]
**Personalisation Hook:** [Reference their webinar participation, specific questions asked, or company challenges mentioned]
**Timeline:** [When you need a response]
**Meeting Details:** [Proposed times/format if requesting a meeting]

Before and After Template Implementation

Before implementing this template, a typical email generation request might be: “Write a follow-up email to Sarah Johnson from TechCorp who attended our CRM webinar. I want to schedule a demo.” The result would be generic and require multiple rounds of revision.

With the template, you simply fill in the variable context: Sarah Johnson from TechCorp, demo scheduling, she asked about data migration during the webinar, need response by Friday, offering Tuesday or Wednesday 2pm slots. The AI now has everything it needs to craft a targeted, professional email that sounds like you wrote it.

The template also enables variations. The same fixed and session context works for thank-you emails (change the objective to “express appreciation and provide resources”), nurture emails (change objective to “share relevant case study”), or re-engagement emails (change objective to “check in and offer assistance”). You’re not starting from scratch each time; you’re adapting a proven framework.

Example 2: Systems Architecture Design Template

# SYSTEMS ARCHITECTURE TEMPLATE

## FIXED CONTEXT

**Role:** Senior Software Architect focused on scalability and maintainability
**Design Philosophy:** Prefer simple solutions over clever ones, always consider operational complexity
**Quality Standards:** All recommendations must address security, performance, and monitoring
**Decision Framework:** Problem → Constraints → Options → Recommendation → Risks

## SESSION CONTEXT

**System:** Customer Data Platform for mid-market SaaS companies
**Project Goal:** Design scalable platform for 10x user growth over 2 years
**Success Metrics:** Handle 10M events/day with less than 200ms response time, 99.9% uptime
**Scale Requirements:** 10,000 active users, 1M events/day, 50GB data growth/month
**Technology Stack:** Node.js/TypeScript, PostgreSQL, AWS infrastructure
**Team Context:** 3 senior engineers, 1 junior developer, 6-month delivery timeline
**Budget Constraints:** £30k for new infrastructure, prefer managed services over custom solutions

## VARIABLE CONTEXT

**Design Challenge:** [Specific component or integration being designed]
**Functional Requirements:** [What the system must do]
**Non-functional Requirements:** [Performance, security, compliance needs]
**Integration Points:** [Existing systems this must connect with]
**Unique Constraints:** [Special requirements for this particular component]

What This Template Delivers

This template transforms architecture discussions from vague brainstorming sessions into structured decision-making processes. When you need to design a new API gateway, you fill in the variable context with the specific requirements and constraints. The AI response includes options analysis, security considerations, and operational implications because those standards are baked into the fixed context.

The session context ensures recommendations stay realistic. The AI knows you’re working with a small team and limited budget, so it won’t suggest solutions that require dedicated DevOps engineers or expensive enterprise software. It understands your technology stack, so suggestions integrate naturally with existing systems.

The template also creates consistency across architecture decisions. Whether you’re designing data pipelines, API endpoints, or background job systems, the same decision framework applies. This consistency makes it easier for team members to understand and maintain the systems you design.

Example 3: Product Requirements Document Template

# PRD GENERATION TEMPLATE

## FIXED CONTEXT

**Role:** Senior Product Manager with technical background
**Audience:** Writing for junior developers and cross-functional stakeholders
**Quality Bar:** Requirements must be testable and unambiguous
**Process:** Always ask clarifying questions before writing requirements
**Standards:** Include user stories, acceptance criteria, and success metrics

## SESSION CONTEXT

**Product:** B2B Project Management Platform
**Primary Users:** Small team leads managing 5-15 people
**Current Focus:** Improving user onboarding and time-to-first-value
**Project Goal:** Reduce user activation friction and increase feature adoption
**Success Metric:** Reduce time from signup to first meaningful action by 50%
**Timeline:** 6-week development cycles, quarterly feature releases

## VARIABLE CONTEXT

**Feature Request:** [Description of requested functionality]
**User Problem:** [Specific pain point this feature addresses]
**Business Justification:** [Why this feature matters to company goals]
**Priority Level:** [Must-have vs nice-to-have vs future consideration]
**Technical Constraints:** [Any known limitations or dependencies]

## CLARIFICATION CHECKLIST

Before writing the PRD, confirm:

- [ ] User journey is clearly defined
- [ ] Success criteria are measurable
- [ ] Edge cases have been considered
- [ ] Integration points are identified
- [ ] Resource requirements are realistic

Template Superpowers

This template embeds your PRD methodology directly into the prompt. The AI knows to ask clarifying questions before jumping into requirements writing. It understands your quality standards and target audience, so the output matches your company’s documentation style automatically.

The clarification checklist is particularly powerful because it catches common oversights before they become problems. Instead of remembering to ask about edge cases, the template reminds the AI to verify this information upfront.

Session context keeps requirements grounded in reality. The AI knows you’re working in six-week cycles with small development teams, so it won’t suggest features that require months of development or specialised expertise you don’t have.

Advanced Template Techniques

Once you’ve mastered basic templates, several advanced techniques can make them even more powerful. These aren’t essential for getting started, but they’re worth understanding as your template library grows more sophisticated.

Template Composition

Template Composition allows you to build larger templates from smaller, reusable components. You might have a standard “Technical Context” block that includes your preferred programming languages, deployment platforms, and coding standards. This block can be included in multiple templates (API design, code review, system architecture) without duplication.

Composition prevents templates from becoming unwieldy. Instead of one massive template that covers every possible scenario, you create focused templates that can be combined as needed. A project planning template might include your standard project management approach, stakeholder identification process, and risk assessment framework as separate, reusable blocks.

Conditional Logic

Conditional Logic helps templates adapt to different situations. You might include instructions like “If this is a customer-facing feature, include accessibility requirements” or “If budget is under £10k, focus on existing tools rather than custom development”. This creates smart templates that adjust their output based on the context you provide.

Conditional logic is particularly useful for templates that serve multiple audiences or use cases. An email template might have different tone instructions for prospects versus existing customers, or a code review template might have different standards for prototype code versus production systems.

Version Control and Evolution

Version Control becomes crucial as templates evolve. Like any important document, templates improve through use and feedback. You might discover that your initial template misses important context or includes unnecessary detail. Version control (even as simple as dating your templates and keeping old versions) helps you iterate systematically.

Good version control also enables experimentation. You can test different approaches to see which produces better results, then update your templates accordingly. This continuous improvement transforms templates from static documents into living tools that get better over time.

Team Templates and Organisational Knowledge

Team Templates unlock the real power of this approach. When multiple people use the same templates, outputs become consistent across the entire team. Everyone’s AI-generated emails sound like they come from the same company. All architecture decisions follow the same framework. PRDs have consistent structure and quality.

Sharing templates also enables collective learning. When someone discovers a better way to structure a particular type of prompt, the improvement benefits everyone. Templates become a form of organisational knowledge that survives team changes and scales with company growth.

Template Libraries emerge naturally as you create more templates for different situations. You might have templates for strategic planning, technical documentation, customer communication, and project management. Organising these libraries (with clear naming conventions and usage notes) makes them easy to find and apply.

The most effective template libraries include examples and usage guidelines. New team members can see not just what the template looks like, but how it’s been used successfully in the past. This accelerates adoption and reduces the learning curve for new users.

Enterprise Template Architecture

Once you’ve mastered basic templates, you can architect sophisticated multi-phase workflows that replace entire manual processes. These enterprise-grade templates move beyond simple context management to become powerful automation tools that can span multiple interactions, enforce quality standards, and integrate with existing systems.

Multi-Phase Execution Patterns

Advanced templates can orchestrate complex workflows that unfold over multiple interactions. Instead of trying to accomplish everything in a single exchange, you can design templates that guide users through structured processes with built-in checkpoints and validation steps.

Consider a template for technical specification creation that works in phases: first generating high-level architecture decisions, then waiting for stakeholder approval, then drilling down into detailed implementation requirements. Each phase builds on the previous one whilst allowing for course corrections based on feedback.

# TECHNICAL SPECIFICATION WORKFLOW

## FIXED CONTEXT

**Role:** Senior Technical Lead responsible for architecture decisions
**Standards:** All specifications must include security, performance, and maintenance considerations
**Approval Process:** Architecture decisions require stakeholder sign-off before implementation details

## SESSION CONTEXT

**Project:** [Current system being designed]
**Stakeholders:** [Technical and business decision-makers]
**Constraints:** [Budget, timeline, technology limitations]

## PHASE 1: ARCHITECTURE OVERVIEW

**Objective:** Generate high-level technical approach for stakeholder review
**Output Format:** Executive summary with 3-5 key architectural decisions
**Next Step:** Present to stakeholders, collect feedback, confirm "Go" to proceed

## PHASE 2: DETAILED SPECIFICATION

**Trigger:** User confirms "Go" after stakeholder approval
**Objective:** Break down each architectural decision into implementation requirements
**Output Format:** Detailed technical specification with acceptance criteria

Conditional Logic and Branching

Enterprise templates can include sophisticated decision trees that adapt based on user responses, project characteristics, or external constraints. This creates intelligent workflows that guide users down appropriate paths without overwhelming them with irrelevant options.

## CONDITIONAL REQUIREMENTS

**If project budget > £100k:** Include comprehensive testing strategy and CI/CD pipeline
**If team size < 3 developers:** Focus on simple, maintainable solutions over complex optimisations
**If external API integration required:** Add security review and rate limiting considerations
**If mobile compatibility needed:** Include responsive design and performance benchmarks

Structured Output Enforcement

Advanced templates can enforce specific output formats, ensuring consistency across teams and projects. This is particularly valuable for regulatory compliance, documentation standards, or integration with existing tools and processes.

The template specifies not just what information to include, but exactly how it should be formatted, where files should be saved, and what naming conventions to follow. This transforms AI interactions from creative exercises into systematic, repeatable processes that integrate seamlessly with existing workflows.

Interactive Confirmation Points

Strategic pause points in template workflows allow for human oversight at critical decision junctures. Rather than generating everything at once, templates can present intermediate results and wait for explicit approval before proceeding.

This approach prevents the AI from pursuing an incorrect path for an extended period and ensures human expertise guides the most important decisions whilst AI handles the detailed execution. It’s particularly valuable for high-stakes decisions where course correction early in the process saves significant time and resources.

Integration with Existing Systems

Enterprise templates can specify how outputs should integrate with project management tools, documentation systems, version control, and other organisational infrastructure. This might include specific file naming conventions, required metadata, or integration points with existing processes.

Templates can also reference and build upon previous outputs, creating workflows that span multiple documents and maintain consistency across related deliverables. A project planning template might reference previous architectural decisions, whilst a testing template builds upon requirements documents created earlier in the process.

Workflow Orchestration

The most sophisticated templates orchestrate entire project workflows, guiding teams through complex processes that might traditionally require project managers or process specialists. These templates embed organisational knowledge about how work should flow, what checkpoints are required, and how different phases connect.

This represents a fundamental shift from AI as a writing assistant to AI as a workflow engine that can encode and execute sophisticated business processes whilst maintaining the flexibility to adapt to specific project needs.

Beyond Prompts: System Design Implications

The three-layer context framework isn’t just useful for writing better prompts; it has profound implications for how AI systems should be designed. Understanding these implications helps explain why some AI tools feel clunky whilst others feel naturally intuitive.

API Design and Performance Optimisation

API Design can optimise dramatically when built around this framework. Fixed context can be cached at the user or organisation level, reducing the amount of information that needs to be transmitted with each request. Session context can be persisted for the duration of a project or working session, then retrieved as needed. Only variable context needs to be processed fresh each time.

This architectural approach reduces costs (you’re not paying to process the same fixed context repeatedly), improves performance (less data to transfer and process), and enables more sophisticated personalisation (the system accumulates knowledge about how you work over time).

Consider how much more elegant ChatGPT would be if it remembered your role, communication preferences, and current project context between sessions. Instead of starting every conversation with the same preamble, you could jump straight to your specific questions, confident that the context you’ve established previously would be maintained.

User Interface Design

User Interface Design becomes more intuitive when it accommodates these different context layers. Instead of a single text box where users dump everything, interfaces could have separate sections for fixed preferences, current project parameters, and specific requests.

Imagine a UI where you set your fixed context once in your profile (role, communication style, quality standards), establish session context at the start of each project (goals, constraints, stakeholders), then only need to type variable context for each individual interaction. The cognitive load would be dramatically reduced, and the results would be far more consistent.

Enterprise Applications and Multi-User Systems

Multi-User Systems can leverage this framework to provide personalised experiences without requiring users to constantly re-establish their preferences. Each user has their own fixed context, but team members can share session context for collaborative projects. Variable context remains private to each interaction.

This enables AI systems that feel truly personalised whilst still supporting team collaboration. Your AI assistant remembers how you like to work but can seamlessly shift to shared project context when you’re collaborating with colleagues.

Enterprise Applications benefit enormously from this structured approach to context management. Large organisations can establish standard fixed context for different roles (all marketing managers share certain preferences, all developers follow the same coding standards), whilst still allowing individual customisation.

Session context can be linked to project management systems, automatically pulling in relevant background information when team members start working on specific initiatives. This integration could transform how companies use AI tools, shifting from individual productivity aids to collaborative intelligence platforms.

Implementation Guide

Getting started with context templates is straightforward, but a systematic approach will help you build effective habits and avoid common pitfalls.

Choosing Your Starting Point

Choose Your Starting Point by identifying one frequent, repetitive AI interaction that consumes significant time or mental energy. This might be generating weekly status reports, creating technical documentation, drafting customer emails, or planning project features. Starting with high-frequency, high-value interactions ensures you’ll see immediate benefits and build momentum for broader adoption.

Avoid the temptation to create templates for every possible scenario immediately. Focus on one template, use it consistently for two weeks, and refine it based on actual experience before moving to the next one.

Template Creation Process

Template Creation Process begins with careful observation of your current interactions. Save a few examples of successful AI conversations in your chosen area, then analyse what context information you typically provide. Look for patterns: what information do you include every time? What changes between sessions? What’s unique to each specific request?

Draft your initial template by separating this information into the three layers. Start simple; you can always add sophistication later. Test the template with a few real scenarios, comparing the results to your previous approach. The improvement should be immediately obvious.

Iteration and Refinement

Iteration and Refinement happens naturally as you use templates regularly. You’ll notice when important context is missing, when you’re including unnecessary detail, or when the structure could be more logical. Keep notes about these observations and update your templates monthly.

Pay attention to edge cases where your template doesn’t quite fit. These often reveal opportunities to improve the template’s flexibility or create specialised variants for specific situations.

Common Pitfalls to Avoid

The most frequent mistakes include:

• Over-engineering: Creating templates so complex they’re harder to use than the original approach
• Under-specifying: Not providing enough detail for consistent results
• Template abandonment: Failing to update templates as needs evolve
• One-size-fits-all thinking: Trying to create one template that handles every possible scenario

It’s better to have three focused templates than one complicated template that tries to do everything.

Measuring Success

Measuring Success is straightforward: templates should save time, improve consistency, and reduce the mental effort required for AI interactions. Track how long it takes to set up your AI conversations before and after implementing templates. Notice whether your outputs maintain consistent quality and style.

The most important metric is whether you actually use the templates consistently. If you find yourself reverting to ad-hoc prompting, the template probably needs simplification or the use case wasn’t well-chosen.

Conclusion

The three-layer context framework transforms AI interactions from repetitive, inconsistent experiences into efficient, predictable workflows. By separating fixed context (your consistent working style), session context (current project parameters), and variable context (specific requests), you can focus your mental energy on the problems you’re actually trying to solve rather than repeatedly explaining who you are and how you work.

Markdown templates provide an immediately practical way to implement this framework. They require no special software, work with any AI tool, and can be shared across teams to create consistent outputs and collective intelligence. The examples we’ve explored (email generation, systems architecture, and PRD creation) demonstrate how this approach scales from individual productivity to organisational capability.

The Compound Benefits

The compound benefits grow over time. Each template you create saves time and cognitive effort across multiple future interactions. The consistency they provide makes it easier to build reliable workflows and collaborate effectively with colleagues. The structured thinking they encourage leads to better problem-solving and clearer communication.

Perhaps most importantly, this framework represents a shift in how we think about human-AI collaboration. Instead of treating AI tools as blank slates that need complete re-education for every interaction, we can create persistent, evolving relationships that become more valuable over time.

Looking Forward

The implications extend beyond individual prompting to AI system design itself. As tools evolve to support multi-layered context management, we’ll see more sophisticated, personalised AI interactions that feel less like using software and more like working with knowledgeable colleagues who understand our context and preferences.

Start with one template. Choose a frequent, valuable AI interaction and spend fifteen minutes separating the context layers. Use it consistently for a fortnight, refine it based on actual experience, then build from there. Within a month, you’ll wonder how you ever managed without this structured approach to AI collaboration.

The future of AI interaction isn’t about better models or faster processing; it’s about better context management. Get the framework right, and everything else follows.

About the Author

Tim Huegdon is the founder of Wyrd Technology, a consultancy that helps organisations harness AI tools effectively. He specialises in transforming how teams collaborate with AI through systematic, repeatable processes that scale across entire organisations. Tim works with engineering leaders, product teams, and knowledge workers to implement AI-first workflows that reduce cognitive overhead whilst maintaining consistency and quality, helping teams develop sustainable practices that evolve with rapidly advancing AI technology.