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Phase 2: The Pattern Builder — Few-Shot Prompting and Context Control

Prompt Engineering
few-shot prompting
_ _ Lisa Yu_ 0 Comments

Prompt Engineering Mastery Series

Phase 1: Zero-Shot Phase 2: Few-Shot Phase 3: Chain-of-Thought Phase 4: Agents

1. What Phase 2 Actually Is

Few-shot promptingA technique where you provide 2-5 input-output examples to demonstrate the desired pattern, then the model replicates that pattern for new inputs. shifts your approach from telling the model what to do to showing it what you want. You provide 2-5 examples of the input-output pattern you need, then let the model complete the next instance.

The mindset shift? You’re no longer writing instructions. You’re demonstrating a pattern. The model learns the structure, format, and logic from your examples, then replicates that pattern for new inputs.

This phase assumes you’ve mastered zero-shot clarity. If your instructions were already ambiguous, adding examples won’t fix the underlying problem. Research from Brown et al. at OpenAI on GPT-3’s few-shot capabilities showed that pattern quality matters more than pattern quantity. Three clean examples outperform ten inconsistent ones.

2. Core Goal of This Phase

Force adherence to specific formats (like JSON) and styles through demonstrated examples.

You’re done guessing whether the model will follow your formatting instructions. You show it the exact structure three times, and it replicates that structure for the fourth instance. This matters when you need consistent outputs for downstream processing, API integrations, or data pipelines.

Few-shot prompting solves the “it works sometimes” problem. Zero-shot might give you JSON 60% of the time. Few-shot with proper examples hits 95%+ consistency.

3. Key Skills You Must Master

1️⃣ Pattern Recognition

▼

Create consistent input-output pairs that demonstrate the transformation you need. The model identifies the pattern connecting your inputs to outputs, then applies that same transformation to new data.

Bad pattern design mixes formats, varies structure, or includes irrelevant details. Good patterns show the minimum viable transformation with zero variation except the data itself.

Why it matters: The model learns from what you show it, not what you intend. Inconsistent examples teach inconsistent behavior. If your three examples use different JSON key names, the model won’t know which schema to follow.

Pattern structure from OpenAI’s prompt engineering documentation:

  • Input: [Example 1 input]
  • Output: [Example 1 output]
  • Input: [Example 2 input]
  • Output: [Example 2 output]
  • Input: [Example 3 input]
  • Output: [Example 3 output]
  • Input: [New data]
  • Output: (Model completes the pattern)

🎯 Few-Shot Pattern Structure

Example 1:
Input:
Meeting on Tuesday at 3pm about Q4 planning
Output:
{“action”: “meeting”, “deadline”: “Tuesday 3pm”, “topic”: “Q4 planning”}
Example 2:
Input:
Can you send the budget report by Friday EOD?
Output:
{“action”: “send report”, “deadline”: “Friday EOD”, “topic”: “budget report”}
Example 3:
Input:
Lunch with Sarah next Monday at noon to discuss the merger
Output:
{“action”: “lunch meeting”, “deadline”: “Monday noon”, “topic”: “merger discussion”}
Now the model applies this pattern to new input ↓
Input:
Review the security audit findings before tomorrow’s board meeting
Output:
Model generates matching JSON structure…
Pattern Consistency: Notice all three examples use the exact same JSON structure (same keys, same order, same data types). This 95%+ consistency is what makes few-shot work.

2️⃣ Persona Design

▼

Assign the model a role to control tone, domain knowledge, and response style. “Act as a security expert” changes how the model weighs technical precision versus general explanations. “Act as a technical writer” shifts toward clear documentation over conversational responses.

Research from Stanford on role-based prompting demonstrates that explicit role assignment improves domain-specific accuracy. The model draws from training data associated with that professional context.

Why it matters: Generic responses waste tokens and miss domain requirements. A security expert identifies CVE classifications. A technical writer follows documentation standards. The model can’t read your mind about which lens to apply.

Effective persona structure:

  • You are a [role] with expertise in [domain].
  • When analyzing [topic], focus on [specific aspects].
  • Use [tone/style] appropriate for [audience].

Don’t write “act as an expert” without defining the expertise domain. “Expert” means nothing without context.

👤 Build Your Persona Prompt

3️⃣ Context Management

▼

Paste relevant background information directly into the prompt so the model bases responses on provided facts instead of training data. This is the foundation of Retrieval-Augmented Generation (RAG), where you retrieve relevant documents then include them in the prompt.

The model’s training data has a cutoff date. It doesn’t know your company policies, recent events, or proprietary information. Context injection fills those gaps.

Why it matters: Models hallucinate when they lack information but try to answer anyway. Providing context reduces hallucinations by giving the model actual data to reference. Lewis et al.’s RAG paper showed retrieval-augmented approaches significantly reduced factual errors.

Context structure:

  • Use the following context to answer the question.
  • Do not use information outside this context.
  • Context: """ [Relevant documents or data] """
  • Question: [User question]

The delimiter separation (""") prevents the model from treating instructions as context or vice versa.

📄 Context Injection Template (RAG)

1 Set Instructions
Use only the information in the context below to answer the question. If the answer is not in the context, state “Information not available in provided context.”
2 Inject Context with Delimiters
Context: “”” [Paste your relevant documents, data, or information here] Example: Tech Jacks Solutions Remote Work Policy (Effective January 2025) – Employees may work remotely up to 3 days per week – Core hours: 10am-3pm local time for team collaboration – VPN required for all remote connections – Quarterly in-person meetings mandatory “””
3 Ask Your Question
Question: What is our company’s remote work policy?
✓ Why This Structure Works:
  • Explicit boundaries: Triple quotes (“””) separate instructions from context
  • Prevents hallucination: “Use only” instruction constrains the model
  • Handles unknowns: Specifies what to say when answer isn’t in context
  • RAG foundation: This is the core pattern for Retrieval-Augmented Generation

4. Practical Examples

Example 1: Structured Data Extraction

Weak few-shot attempt:

Extract the key info from this email and format it.

Email 1: "Meeting on Tuesday at 3pm about Q4 planning"
Extracted: Tuesday 3pm Q4

Email 2: "Can you send the budget report by Friday EOD?"
Extracted: Budget report Friday

Email 3: "Lunch with Sarah next Monday at noon to discuss the merger"
Output:

Improved few-shot prompt:

Extract information from emails into JSON with keys: action, deadline, topic.

Input: "Meeting on Tuesday at 3pm about Q4 planning"
Output: {"action": "meeting", "deadline": "Tuesday 3pm", "topic": "Q4 planning"}

Input: "Can you send the budget report by Friday EOD?"
Output: {"action": "send report", "deadline": "Friday EOD", "topic": "budget report"}

Input: "Lunch with Sarah next Monday at noon to discuss the merger"
Output: {"action": "lunch meeting", "deadline": "Monday noon", "topic": "merger discussion"}

Input: "Review the security audit findings before tomorrow's board meeting"
Output:

Why it works: Consistent JSON structure across all examples. Same keys in the same order. Same data types. The model learns the exact schema and replicates it. The weak version has no consistent format, so the model guesses at structure.

Example 2: Persona-Driven Analysis

Weak personaA defined role (e.g., “security engineer”) assigned to the model that controls tone, domain knowledge, and response style.:

Act as an expert and analyze this code for security issues.

Improved persona:

You are a security engineer specializing in OWASP Top 10 vulnerabilities and secure coding practices.
Analyze code for security issues following this structure:
- Vulnerability type (reference OWASP category)
- Severity (Critical/High/Medium/Low based on CVSS)
- Affected code section
- Remediation steps with code examples

Use technical precision appropriate for senior developers.

Why it works: Defines expertise domain (OWASP, secure coding), output structure, severity framework (CVSS scoring), and audience level. The weak version just says “expert” without specifying expertise type or output requirements.

Example 3: Context-Augmented Response

Without context (hallucinationWhen the model generates plausible-sounding but incorrect information, often because it lacks actual knowledge but tries to answer anyway. risk):

What is our company's remote work policy?

With context:

Use only the information in the context below to answer the question.
If the answer is not in the context, state "Information not available in provided policy."

Context:
"""
Tech Jacks Solutions Remote Work Policy (Effective January 2025)
- Employees may work remotely up to 3 days per week
- Core hours: 10am-3pm local time for team collaboration
- VPN required for all remote connections
- Quarterly in-person meetings mandatory
"""

Question: What is our company's remote work policy?

Why it works: Provides the actual policy text, prevents hallucination with explicit instructions, includes the policy effective date for verification. Without context, the model might generate plausible-sounding but completely fabricated policy details.

5. Common Mistakes at This Phase

Inconsistent example formats. Showing three different output structures teaches the model that format doesn’t matter. Pick one schema and use it for every example.

Too many examples. Five examples usually work better than ten. Beyond five, you’re wasting tokens without improving pattern recognitionThe model’s ability to identify consistent structures in your examples and apply the same transformation to new data.. Research on few-shot learning shows diminishing returns after 5-7 examples.

Vague personas. “Act as an expert” tells the model nothing. “Act as a penetration tester analyzing web applications using OWASP testing methodology” specifies the expertise lens.

Context dumping without structure. Pasting ten pages of documentation with no guidance wastes tokens. Extract the relevant sections, use clear delimiters, and explicitly instruct the model to use only that context.

Conflicting instructions and examples. Your instruction says “use active voice” but your examples are written in passive voice. The model follows the examples, not the instructions.

📊 Example Quantity vs. Effectiveness

Number of Examples: 3
85%
Good Range
3 examples provide strong pattern recognition with efficient token usage.

6. How to Know You Are Ready for the Next Phase

✓ Ready for Phase 3?

Check off each skill as you master it:

Format adherence: Your few-shot prompts produce outputs matching your specified structure (JSON, CSV, specific text format) without post-processing 95%+ of the time
Persona control: You can switch between technical depth levels (beginner vs. practitioner detail) by modifying role definitions without changing the core prompt
Context accuracy: When you inject specific context, the model references that context rather than generating responses from training data
Pattern efficiency: You’ve reduced example count to the minimum needed (2-5 examples) based on complexity, not guesswork
0/4 skills mastered. Keep practicing!

If you’re still getting inconsistent formats, struggling to control response style, or seeing hallucinations despite providing context, stay in Phase 2. The next phase (reasoning strategies) assumes the model can already follow patterns reliably. Adding chain-of-thought prompting on top of inconsistent pattern-following just creates verbose inconsistency.

Few-shot prompting transforms unreliable outputs into predictable components. Every data pipeline, every API integration, every automated workflow depends on consistent structure. Master pattern demonstration before attempting complex reasoning chains.

Ready for advanced reasoning strategies?

Master chain-of-thought prompting, error reduction, and self-correction techniques to build more reliable AI systems

Continue to Phase 3: Logic Architect →

Sources

Research Papers

Official Documentation

Standards & Frameworks

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Author

Lisa Yu

I am an AWS Cloud Practitioner certified, AI and cybersecurity researcher, and content creator with over a decade of experience in IT. My work focuses on making complex topics like artificial intelligence, cloud computing, cybersecurity, and AI governance easier to understand for non-technical audiences. Through research-driven articles, guides, and visual content, I help individuals and organizations build practical knowledge they can actually use. I am especially interested in responsible AI, emerging technologies, and bridging the gap between technical experts and everyday users.

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