The Shift Happening Now
For the past few years, the conventional wisdom has been clear: learn prompt engineering. Master the art of phrasing requests to AI just right. Use the magic words. Add “think step by step.” Specify a persona. The advice was always about how you say it.
That advice isn’t wrong. But it’s incomplete. Practitioners who work with AI daily have noticed something: the phrasing of a request matters far less than what information accompanies it. A perfectly worded prompt with no context still produces generic output. A simply worded prompt surrounded by the right context produces something genuinely useful.
This realization has a name: context engineering. It’s the emerging discipline of designing the entire information environment that surrounds an AI interaction, not just the words you type into the input box. Think of it this way: prompt engineering is choosing the right words in a meeting. Context engineering is deciding who should be in the room, what documents to bring, what agenda to set, and what decisions need to be made before the meeting even starts.
Insight
What Is Context Engineering?
Context engineering is the practice of curating, structuring, and sequencing the information that goes into an AI interaction. Where prompt engineering focuses on the instruction itself, context engineering focuses on everything that surrounds it: the background knowledge, the constraints, the examples, the reference materials, and the output specifications.
It rests on five pillars. Each one addresses a different aspect of designing effective AI context:
Information Selection
Information Structuring
Information Sequencing
Constraint Definition
Output Specification
These pillars are not a checklist to follow mechanically. They’re a mental model for thinking about what makes AI interactions succeed or fail. Most failures trace back to a weakness in one of these five areas.
Pillar 1: Information Selection
The most common mistake people make with AI is not providing too little information. It’s providing the wrong information. Information selection is about choosing what to include based on what actually matters for the task at hand, and having the discipline to leave out what doesn’t.
Ask three questions before adding any piece of context: Does the AI need this to complete the task? Would removing this change the quality of the output? Is this specific enough to be actionable?
Write a marketing email for our product. Our company was founded in 2019. We have offices in San Francisco and London. Our CEO previously worked at Google. We raised a Series B in 2023. Our product helps teams manage projects. We have 2,000 customers. Our mascot is a fox named Felix.
Write a marketing email announcing our new task dependency visualization feature. RELEVANT CONTEXT: - Product: B2B project management tool for tech teams (20-200 employees) - Target audience: Engineering managers who currently use Jira or Asana - Key differentiator: Visual dependency mapping that updates in real-time - Customer pain point: Teams lose 5+ hours/week to coordination overhead - Previous launch email stats: 18% open rate, 3.2% click-through rate
Notice the difference. The first prompt includes irrelevant history (founding date, office locations, CEO background, mascot) while missing critical details (target audience, differentiator, pain point). The second prompt includes only what shapes the output.
- The deletion test: If you remove a piece of context and the ideal output wouldn’t change, leave it out
- The specificity check: Replace every vague descriptor with a number, name, or concrete detail
- The audience filter: Would your target reader care about this information?
- The relevance window: Is this information current and applicable to this specific task?
- The actionability test: Can the AI actually use this to improve its output?
Pillar 2: Information Structuring
Once you’ve selected the right information, how you organize it matters more than most people expect. AI models process context as a sequence of tokens. Clear structure helps the model distinguish between different content types, understand hierarchies, and assign appropriate weight to each element.
Think of structuring as giving AI a well-organized filing cabinet instead of a pile of loose papers. Both contain the same information, but one is dramatically easier to work with.
## ROLE You are a senior product analyst specializing in B2B SaaS metrics. ## BACKGROUND Company: TaskFlow (project management SaaS) Stage: Series B, 2,000 customers Segment: Tech companies, 20-200 employees ## DATA TO ANALYZE Monthly active users: 12,400 (up 15% QoQ) Churn rate: 4.2% monthly (industry avg: 5.8%) NPS: 42 (up from 38 last quarter) Feature adoption (dependency view): 34% of active users ## TASK Analyze these metrics and identify: 1. The strongest leading indicator of growth 2. The biggest risk in the next 6 months 3. One metric that needs deeper investigation ## CONSTRAINTS - Base conclusions only on the data provided - Flag assumptions explicitly - Keep analysis under 500 words
- Headers and sections: Use ## or labeled blocks to separate context types (ROLE, BACKGROUND, TASK, CONSTRAINTS)
- Hierarchical nesting: Put high-level context first, then drill into specifics
- Separators: Use --- or blank lines between logical sections
- Labels: Prefix data with descriptive labels (Company:, Metric:, Constraint:)
- Consistent formatting: Use the same structure pattern throughout a single prompt
Pro Tip
Pillar 3: Information Sequencing
Order matters more than most people realize. Research on large language models shows that information placement affects how much weight the model gives it. Information at the beginning and end of context tends to be weighted more heavily than information buried in the middle, a phenomenon sometimes called the “lost in the middle” effect.
Practical sequencing means putting your most critical constraints and instructions where they’ll have the most impact. For most tasks, this means: role and framing first, then background context, then the specific task, then constraints and output requirements last.
Recommended sequence for complex prompts: 1. ROLE / IDENTITY → Who the AI should be (sets the frame) 2. CONTEXT → Background the AI needs 3. SPECIFIC DATA → Numbers, facts, reference material 4. TASK → What to do with all of the above 5. CONSTRAINTS → Boundaries and guardrails 6. OUTPUT FORMAT → How to structure the response This sequence works because it mirrors how humans process briefings: first understand who you are and why you're here, then absorb the background, then receive the assignment, then understand the rules.
- Primacy effect: Information presented first anchors the AI’s approach to the entire task
- Recency effect: Instructions at the end are freshest when the AI begins generating output
- The middle risk: Long context windows can cause information in the middle to receive less weight
- Dependency ordering: Place information that later context depends on earlier in the sequence
- Repeat critical constraints: State the most important guardrails both early (in context) and late (before output format)
Pillar 4: Constraint Definition
Constraints are the guardrails that keep AI output on track. Without them, AI tends to drift toward its training defaults: generic, verbose, and trying to cover every possible angle. Well-defined constraints channel the AI’s capabilities toward your specific needs.
Effective constraints come in several flavors: scope constraints (what to cover), exclusion constraints (what to avoid), quality constraints (standards to meet), and format constraints (structural requirements).
CONSTRAINTS:
- Scope: Focus only on Q4 2024 performance. Do not reference earlier quarters unless directly comparing trends.
- Exclusions: Do not include general industry advice or recommendations that require additional budget.
- Quality: Every claim must reference a specific data point from the provided metrics. Flag any conclusions that require assumptions.
- Length: Executive summary in 200 words max. Detailed analysis in 500 words max.
- Tone boundary: Analytical and direct. No hedging language ("it might be possible that..."). State findings as findings.
- What NOT to do: Do not create visualizations, do not suggest follow-up meetings, do not provide a SWOT analysis.Warning
- Scope constraints: Time periods, topics, data sources to include
- Exclusion constraints: Topics, approaches, or formats to avoid
- Quality constraints: Evidence requirements, accuracy standards, citation rules
- Format constraints: Length limits, structure requirements, style rules
- Behavioral constraints: How to handle uncertainty, what to do with missing data
Pillar 5: Output Specification
Output specification is where context engineering meets practical results. It defines exactly what the deliverable should look like: the format, the sections, the level of detail, and the success criteria. Think of it as providing the AI with a blueprint for the finished product.
The more specific your output specification, the less time you spend reformatting and re-prompting. A good output spec eliminates the “that’s not what I meant” cycle that plagues most AI interactions.
OUTPUT SPECIFICATION: Produce a competitive analysis document with exactly this structure: ## Executive Summary (3-4 sentences) Key findings and primary recommendation. ## Competitor Comparison Table | Feature | Us | Competitor A | Competitor B | Columns: Feature name, our capability (Yes/No/Partial), their capability, notes ## Strengths to Leverage (3-5 bullet points) Each bullet: [Strength] — [How to leverage it in messaging] ## Gaps to Address (3-5 bullet points) Each bullet: [Gap] — [Priority: High/Medium/Low] — [Suggested timeline] ## Recommended Next Steps Numbered list, max 5 items, each actionable within 30 days. FORMAT RULES: - Use markdown throughout - Bold key terms on first use - Keep total length between 800-1200 words - No filler sentences or throat-clearing introductions
- Structure: Define sections, headers, and their order
- Length: Set word counts or ranges for each section
- Format: Specify markdown, bullet points, tables, or prose
- Examples: Provide a sample of what the output should look like
- Anti-patterns: Describe what the output should NOT look like
- Success criteria: How will you evaluate whether the output is good?
Why Prompt Engineering Isn’t Enough
Prompt engineering teaches you to craft better instructions. That’s valuable, but it operates on only one dimension of the problem. Consider this analogy: if you’re trying to get a great meal at a restaurant, prompt engineering is about placing a precise order. Context engineering is about choosing the right restaurant, bringing the right dining companions, communicating your dietary needs in advance, and arriving at the right time.
Here’s the pattern we see repeatedly: someone writes a beautifully phrased prompt that fails because the AI lacked the information it needed to do the job well. The fix isn’t a better prompt. It’s better context.
You are an expert marketing strategist with 20 years of experience. Think step by step. Be creative but practical. Use proven frameworks. I need you to create a comprehensive go-to-market strategy for my product launch. Make it detailed and actionable. Think like a CMO at a Fortune 500 company. Be specific with timelines and budgets. This is extremely important. Take your time and produce your best work.
Create a 90-day go-to-market plan for launching our dependency visualization feature. PRODUCT CONTEXT: - B2B SaaS project management tool, $49-199/mo pricing tiers - Current customer base: 2,000 companies (tech sector, 20-200 employees) - Feature: Real-time visual task dependency mapping - Competitive gap: Asana has basic dependencies; we have visual, real-time updates LAUNCH CONSTRAINTS: - Marketing budget: $15,000 for the launch period - Team: 1 product marketer, 1 content writer, shared designer - Timeline: Feature ships March 1, full launch March 15 - Channels available: Email (22,000 list), blog, LinkedIn, Product Hunt GOALS: - 40% feature adoption among existing users within 90 days - 200 trial signups attributed to launch campaign - 3 customer case studies by day 60 OUTPUT: Week-by-week plan with specific deliverables, owners, and success metrics for each phase.
The first prompt uses every prompt engineering trick in the book: expert persona, chain-of-thought instruction, emphasis markers, quality appeals. But it contains almost zero useful context. The AI will produce a generic strategy that could apply to any product.
The second prompt is plainly written. No persona tricks, no emphasis markers. But it’s packed with specific, relevant context. The AI now has the information it needs to produce a plan that’s actually useful.
Insight
Context Engineering in Practice
Theory is useful, but context engineering is a practical skill. Here are three real-world examples showing how the five pillars work together across different domains.
Example 1: Market Analysis Request
## ROLE Act as a market analyst preparing a briefing for a VP of Product. ## CONTEXT Company: Mid-stage B2B SaaS (project management space) Recent event: Two competitors (Monday.com and ClickUp) released AI-powered features in Q4 2024 Our status: No AI features shipped yet; R&D prototype exists Board meeting: In 3 weeks; VP needs data to support AI investment request ## REFERENCE DATA - Monday.com AI: Auto-generates project timelines from task descriptions (launched Oct 2024) - ClickUp AI: Summarizes project status across multiple views (launched Nov 2024) - Our prototype: AI-powered risk detection for task dependencies (internal testing) ## TASK Analyze the competitive AI feature landscape and recommend whether to accelerate, maintain, or pivot our AI roadmap. ## CONSTRAINTS - Focus only on AI features relevant to project management - Do not recommend specific vendors or tools - All recommendations must be defensible with the provided data - Flag gaps where additional market research is needed ## OUTPUT FORMAT 1. Executive summary (5 sentences max) 2. Competitor AI feature comparison table 3. Our competitive position assessment (strengths, gaps, opportunities) 4. Recommendation with supporting rationale 5. Open questions for the board discussion
Example 2: Code Review Context
## ROLE Senior backend engineer reviewing a pull request for production readiness. ## CODEBASE CONTEXT - Language: TypeScript, Node.js - Framework: Express with clean architecture (controllers → services → repositories) - Database: MongoDB with Mongoose ODM - Auth: JWT tokens, middleware-based authentication - Testing: Jest with 78% coverage requirement ## PR CONTEXT - Feature: Adding rate limiting to the public API - Files changed: 4 (rate-limit middleware, config, 2 test files) - Author: Junior developer (3 months on team) - Sprint deadline: 2 days ## REVIEW FOCUS 1. Security: Does the rate limiting actually prevent abuse? Are there bypass vectors? 2. Performance: What's the overhead per request? Is the storage mechanism scalable? 3. Configuration: Are rate limits configurable per environment? Are defaults sensible? 4. Edge cases: What happens when the rate limit store goes down? Is there a graceful fallback? ## CONSTRAINTS - Be constructive, not critical. This is a junior developer. - Categorize feedback as: [MUST FIX], [SHOULD FIX], [CONSIDER], [NICE TO HAVE] - Do not rewrite the code. Explain what to change and why. - Limit to 10 most important comments.
Example 3: Brand-Aligned Content Context
## ROLE Brand content writer for a B2B SaaS company. ## BRAND VOICE CONTEXT - Personality: Knowledgeable but approachable. Think "smart friend who works in tech." - Do: Use plain language, concrete examples, short sentences. Say "use" not "utilize." - Don't: Use buzzwords (leverage, synergy, game-changing), hype language, or exclamation marks. - Tone range: Confident to warm. Never aggressive, never stiff. - Reading level: Target 8th grade (Flesch-Kincaid). ## CONTENT CONTEXT - Platform: Company blog - Topic: How to run more effective sprint retrospectives - Audience: Engineering managers at mid-size tech companies - Goal: Establish thought leadership; soft CTA to product - SEO target: "sprint retrospective best practices" (1,900 monthly searches) ## REFERENCE MATERIAL - Our product supports retrospective templates and action item tracking - Top-ranking competitor article: 2,400 words, listicle format, generic advice - Our differentiator: We can be more specific because our audience is narrower ## CONSTRAINTS - Length: 1,500-1,800 words - Structure: Introduction, 5-6 actionable sections, conclusion with soft product mention - Include at least 2 specific examples from real engineering teams (anonymized) - Internal linking: Reference our project planning guide and team communication post - Do not mention competitors by name
Context Engineering vs Prompt Engineering
Context engineering doesn’t replace prompt engineering. It builds on top of it. Here’s how the two disciplines differ in mindset and approach:
| Dimension | Prompt Engineering | Context Engineering |
|---|---|---|
| Focus | How you phrase the request | What information accompanies the request |
| Analogy | Writing a clear email | Preparing a complete briefing package |
| Key skill | Concise, precise language | Information architecture and curation |
| Failure mode | AI misunderstands the task | AI lacks the knowledge to do the task well |
| Improvement lever | Rewrite the instruction | Redesign the information environment |
| Scalability | Each prompt is crafted individually | Context patterns can be templated and reused |
| Mental model | “How do I ask better?” | “What does the AI need to know?” |
Common Context Failures
When AI output disappoints, the root cause is almost always a context failure, not an AI limitation. Here are the four patterns that account for most problems:
1. Context Starvation
The AI doesn’t have enough information to produce useful output. The prompt says “write me a strategy” without specifying the company, goals, constraints, or audience. The AI fills the gaps with generic assumptions.
Fix: Use the five pillars as a checklist. Which ones are missing?
2. Context Flooding
Too much information, much of it irrelevant. When you paste in an entire document and say “summarize this,” the AI has no way to know what matters to you. Important details get lost in the noise.
Fix: Apply the deletion test. Remove anything that wouldn’t change the ideal output.
3. Context Disorder
The right information is present but poorly organized. Constraints are mixed with background. The task appears before the context it depends on. The AI struggles to assign the right weight to each piece.
Fix: Restructure using labeled sections and the recommended sequencing order.
4. Missing Guardrails
The AI produces output that technically answers the question but misses the mark because boundaries were never defined. It writes 2,000 words when you needed 200. It includes competitor mentions you wanted to avoid. It uses a formal tone for a casual audience.
Fix: Add explicit constraints, especially “what NOT to do” instructions.
Quick Reference: The Context Engineering Checklist
Before sending any important prompt, run through this checklist. It takes 60 seconds and consistently improves output quality.
| Pillar | Ask Yourself | Red Flag If Missing |
|---|---|---|
| Selection | Does AI have the specific facts it needs? | Output is generic and could apply to anyone |
| Structuring | Is the information clearly organized with labels? | AI confuses context types or ignores details |
| Sequencing | Are the most important elements first and last? | Key requirements get overlooked in the middle |
| Constraints | Have I defined what NOT to include? | Output includes unwanted content or wrong format |
| Output Spec | Have I described the exact deliverable? | Output needs heavy reformatting before use |
Context Engineering Template - Copy and Adapt: ## ROLE [Who should the AI be for this task?] ## CONTEXT [Background: What situation or project is this for?] [Data: What specific facts, metrics, or reference material does the AI need?] ## TASK [What specific deliverable do you need? Use an action verb.] ## CONSTRAINTS - Scope: [What to include / exclude] - Quality: [Standards, evidence requirements] - Length: [Word count or section limits] - What NOT to do: [Explicit exclusions] ## OUTPUT FORMAT [Exact structure of the expected deliverable] [Section headers, lengths, format requirements]
Next Steps
Context engineering is not a skill you learn once. It’s a practice you develop over time. The more you work with AI, the better your instincts become for what information matters, how to structure it, and where the common failure points hide.
But here’s the thing: you don’t have to do all of this manually. The five pillars we covered, selecting the right information, structuring it clearly, sequencing it for impact, defining constraints, and specifying outputs, are exactly what AskSmarter.ai’s prompt builder does automatically. It asks you smart questions to extract the context that matters, then engineers that context into a well-structured prompt.
You bring the knowledge about your task. We handle the context engineering.
Context engineering, automated
AskSmarter’s prompt builder applies context engineering principles to every prompt it creates. Answer questions about your task, and it selects, structures, and sequences the right context automatically. No frameworks to memorize. No staring at an empty prompt box.
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