AI: Context Dos and Don'ts
tl;dr
- Start with the end in mind: Tell AI what you want to achieve before diving into details
- Be specific about constraints: Include limitations, requirements, and preferences upfront
- Use examples to clarify intent: Show, don't just tell, what you're looking for
- Refresh context in long conversations: Periodically summarize key points to maintain accuracy
- Avoid context overload: Too much irrelevant information confuses AI just like it confuses humans
In our previous article, we explored how context transforms AI responses from generic to specific. Now let's get practical. What specific techniques consistently produce better AI responses? After analyzing thousands of interactions, clear patterns emerge.
Start with Your Objective
DO: Lead with what you're trying to achieve
"I need 5 LinkedIn posts for a custom software company targeting healthcare CFOs who are evaluating digital transformation options. We want to emphasize ROI and security."
When AI understands your end goal immediately, it can align every aspect of its response to that objective. Research shows that goal-oriented prompting significantly improves response relevance and accuracy[1].
DON'T: Bury your actual request
Instead of asking for "marketing help" or "social media assistance" while providing extensive company background before stating what you actually need, get to the point. Forcing AI to infer your actual request from paragraphs of context reduces response quality.
Define Your Constraints
DO: Include limitations and requirements upfront
"Create a project timeline for our app redesign. Constraints: $50K budget, 3-month deadline, team of 4 developers, must maintain current system during transition."
Constraints aren't restrictions—they're parameters that help AI provide realistic, implementable suggestions rather than theoretical solutions[2].
DON'T: Add constraints after receiving a response
Rather than saying "create a project timeline" and then mentioning "oh, but we only have $50K" after receiving a detailed plan, include all limitations from the start. Revealing constraints after the fact wastes time and computational resources.
Provide Clear Examples
DO: Demonstrate what you're looking for
"Write a customer email similar to this style: 'Hi Sarah, Thanks for reaching out about the delayed shipment. I've personally tracked your order and...' Keep it personal, solution-focused, and under 100 words."
Examples efficiently communicate tone, style, and format preferences that would otherwise require lengthy explanations. This approach, known as few-shot prompting, has been shown to dramatically improve AI performance[3].
DON'T: Rely solely on abstract descriptions
Instead of requesting "write a friendly customer email" without any examples or specific guidance, provide concrete demonstrations. Abstract terms like "friendly" or "professional" mean different things to different organizations.
Manage Long Conversations
DO: Summarize periodically
"To recap our discussion: we're building a mobile app for pet owners, targeting iOS first, with GPS tracking and vet appointment scheduling as core features. Now, regarding user onboarding..."
This technique prevents important details from being pushed out of the AI's context window as conversations extend. Research indicates that AI models perform best when relevant information is placed at the beginning or end of the context window[4].
DON'T: Assume perfect recall
Rather than continuing to add new topics and requirements while assuming AI remembers every detail from an hour-long conversation, periodically refresh key points. AI systems have context limitations that affect response accuracy over long exchanges.
Balance Information Quantity
DO: Provide relevant context
"We're updating our employee handbook section on remote work. Current policy allows 2 days/week remote. Industry trend is moving to flexible arrangements. Need to balance employee preferences with collaboration needs."
This gives AI sufficient information to provide nuanced, applicable suggestions without overwhelming the system.
DON'T: Include everything possible
Instead of providing your entire company history, every policy iteration since inception, and all tangential considerations "just in case," focus on what's directly relevant. Studies show that providing a focused set of relevant information yields better performance than overwhelming models with excessive data[5].
Practical Implementation
These techniques work across all AI interactions, from simple queries to complex project planning. The key is consistency. Start with clear objectives, provide relevant constraints, and structure information logically.
Organizations that train their teams on these practices report significant improvements in AI interaction efficiency. The time invested in proper context formulation pays dividends in reduced back-and-forth and higher-quality initial responses.
Some Advanced Tips
- Structure complex requests: For multi-part projects, organize requirements into clear categories (audience, budget, timeline, constraints) rather than mixing everything together
- Include domain context: Mention industry-specific requirements like "HIPAA compliance for healthcare" or "SOC 2 for financial services" to get appropriately tailored responses
- Build iteratively: Instead of starting new conversations for refinements, build on AI's responses: "That's helpful. Now adjust for mobile-first users..."
What's Next?
With practical techniques covered, our next article examines the technical architecture behind context windows: how they work, why they have limits, and what innovations are expanding these boundaries.
Final Thoughts
Effective AI context isn't about technical expertise—it's about clear communication. These practices transform AI from a tool that might help into one that consistently delivers value.
The investment in learning these techniques is minimal, but the impact on AI interaction quality is substantial. Start with one or two practices, observe the improvement, then gradually incorporate others. Better context leads to better results, every time.
Continue exploring our AI Context series: Understanding Context | Dos and Don'ts | Technology Basics | Business Applications | Industry Trends
References
- Prompt Engineering Best Practices: Tips, Tricks, and Tools – DigitalOcean
- 12 prompt engineering best practices and tips – TechTarget
- Prompt Engineering Techniques – IBM
- What is a context window? – IBM
- What is a Context Window in AI? – Zilliz
