Senior Prompt Engineer

Senior Prompt Engineer

Prompt engineering patterns, LLM evaluation frameworks, and agentic system design.

Table of Contents

• Quick Start
• Tools Overview
  • Prompt Optimizer
  • RAG Evaluator
  • Agent Orchestrator
• Prompt Engineering Workflows
  • Prompt Optimization Workflow
  • Few-Shot Example Design
  • Structured Output Design
• Reference Documentation
• Common Patterns Quick Reference

Quick Start

# Analyze and optimize a prompt file
python scripts/prompt_optimizer.py prompts/my_prompt.txt --analyze

# Evaluate RAG retrieval quality
python scripts/rag_evaluator.py --contexts contexts.json --questions questions.json

# Visualize agent workflow from definition
python scripts/agent_orchestrator.py agent_config.yaml --visualize

Tools Overview

1. Prompt Optimizer

Analyzes prompts for token efficiency, clarity, and structure. Generates optimized versions.

Input: Prompt text file or string Output: Analysis report with optimization suggestions

Usage:

# Analyze a prompt file
python scripts/prompt_optimizer.py prompt.txt --analyze

# Output:
# Token count: 847
# Estimated cost: $0.0025 (GPT-4)
# Clarity score: 72/100
# Issues found:
#   - Ambiguous instruction at line 3
#   - Missing output format specification
#   - Redundant context (lines 12-15 repeat lines 5-8)
# Suggestions:
#   1. Add explicit output format: "Respond in JSON with keys: ..."
#   2. Remove redundant context to save 89 tokens
#   3. Clarify "analyze" -> "list the top 3 issues with severity ratings"

# Generate optimized version
python scripts/prompt_optimizer.py prompt.txt --optimize --output optimized.txt

# Count tokens for cost estimation
python scripts/prompt_optimizer.py prompt.txt --tokens --model gpt-4

# Extract and manage few-shot examples
python scripts/prompt_optimizer.py prompt.txt --extract-examples --output examples.json

2. RAG Evaluator

Evaluates Retrieval-Augmented Generation quality by measuring context relevance and answer faithfulness.

Input: Retrieved contexts (JSON) and questions/answers Output: Evaluation metrics and quality report

Usage:

# Evaluate retrieval quality
python scripts/rag_evaluator.py --contexts retrieved.json --questions eval_set.json

# Output:
# === RAG Evaluation Report ===
# Questions evaluated: 50
#
# Retrieval Metrics:
#   Context Relevance: 0.78 (target: >0.80)
#   Retrieval Precision@5: 0.72
#   Coverage: 0.85
#
# Generation Metrics:
#   Answer Faithfulness: 0.91
#   Groundedness: 0.88
#
# Issues Found:
#   - 8 questions had no relevant context in top-5
#   - 3 answers contained information not in context
#
# Recommendations:
#   1. Improve chunking strategy for technical documents
#   2. Add metadata filtering for date-sensitive queries

# Evaluate with custom metrics
python scripts/rag_evaluator.py --contexts retrieved.json --questions eval_set.json \
    --metrics relevance,faithfulness,coverage

# Export detailed results
python scripts/rag_evaluator.py --contexts retrieved.json --questions eval_set.json \
    --output report.json --verbose

3. Agent Orchestrator

Parses agent definitions and visualizes execution flows. Validates tool configurations.

Input: Agent configuration (YAML/JSON) Output: Workflow visualization, validation report

Usage:

# Validate agent configuration
python scripts/agent_orchestrator.py agent.yaml --validate

# Output:
# === Agent Validation Report ===
# Agent: research_assistant
# Pattern: ReAct
#
# Tools (4 registered):
#   [OK] web_search - API key configured
#   [OK] calculator - No config needed
#   [WARN] file_reader - Missing allowed_paths
#   [OK] summarizer - Prompt template valid
#
# Flow Analysis:
#   Max depth: 5 iterations
#   Estimated tokens/run: 2,400-4,800
#   Potential infinite loop: No
#
# Recommendations:
#   1. Add allowed_paths to file_reader for security
#   2. Consider adding early exit condition for simple queries

# Visualize agent workflow (ASCII)
python scripts/agent_orchestrator.py agent.yaml --visualize

# Output:
# ┌─────────────────────────────────────────┐
# │            research_assistant           │
# │              (ReAct Pattern)            │
# └─────────────────┬───────────────────────┘
#                   │
#          ┌────────▼────────┐
#          │   User Query    │
#          └────────┬────────┘
#                   │
#          ┌────────▼────────┐
#          │     Think       │◄──────┐
#          └────────┬────────┘       │
#                   │                │
#          ┌────────▼────────┐       │
#          │   Select Tool   │       │
#          └────────┬────────┘       │
#                   │                │
#     ┌─────────────┼─────────────┐  │
#     ▼             ▼             ▼  │
# [web_search] [calculator] [file_reader]
#     │             │             │  │
#     └─────────────┼─────────────┘  │
#                   │                │
#          ┌────────▼────────┐       │
#          │    Observe      │───────┘
#          └────────┬────────┘
#                   │
#          ┌────────▼────────┐
#          │  Final Answer   │
#          └─────────────────┘

# Export workflow as Mermaid diagram
python scripts/agent_orchestrator.py agent.yaml --visualize --format mermaid

Prompt Engineering Workflows

Prompt Optimization Workflow

Use when improving an existing prompt's performance or reducing token costs.

Step 1: Baseline current prompt

python scripts/prompt_optimizer.py current_prompt.txt --analyze --output baseline.json

Step 2: Identify issues Review the analysis report for:

• Token waste (redundant instructions, verbose examples)
• Ambiguous instructions (unclear output format, vague verbs)
• Missing constraints (no length limits, no format specification)

Step 3: Apply optimization patterns

Step 4: Generate optimized version

python scripts/prompt_optimizer.py current_prompt.txt --optimize --output optimized.txt

Step 5: Compare results

python scripts/prompt_optimizer.py optimized.txt --analyze --compare baseline.json
# Shows: token reduction, clarity improvement, issues resolved

Step 6: Validate with test cases Run both prompts against your evaluation set and compare outputs.

Few-Shot Example Design Workflow

Use when creating examples for in-context learning.

Step 1: Define the task clearly

Task: Extract product entities from customer reviews
Input: Review text
Output: JSON with {product_name, sentiment, features_mentioned}

Step 2: Select diverse examples (3-5 recommended)

Step 3: Format consistently

Example 1:
Input: "Love my new iPhone 15, the camera is amazing!"
Output: {"product_name": "iPhone 15", "sentiment": "positive", "features_mentioned": ["camera"]}

Example 2:
Input: "The laptop was okay but battery life is terrible."
Output: {"product_name": "laptop", "sentiment": "mixed", "features_mentioned": ["battery life"]}

Step 4: Validate example quality

python scripts/prompt_optimizer.py prompt_with_examples.txt --validate-examples
# Checks: consistency, coverage, format alignment

Step 5: Test with held-out cases Ensure model generalizes beyond your examples.

Structured Output Design Workflow

Use when you need reliable JSON/XML/structured responses.

Step 1: Define schema

{
  "type": "object",
  "properties": {
    "summary": {"type": "string", "maxLength": 200},
    "sentiment": {"enum": ["positive", "negative", "neutral"]},
    "confidence": {"type": "number", "minimum": 0, "maximum": 1}
  },
  "required": ["summary", "sentiment"]
}

Step 2: Include schema in prompt

Respond with JSON matching this schema:
- summary (string, max 200 chars): Brief summary of the content
- sentiment (enum): One of "positive", "negative", "neutral"
- confidence (number 0-1): Your confidence in the sentiment

Step 3: Add format enforcement

IMPORTANT: Respond ONLY with valid JSON. No markdown, no explanation.
Start your response with { and end with }

Step 4: Validate outputs

python scripts/prompt_optimizer.py structured_prompt.txt --validate-schema schema.json

Reference Documentation

Common Patterns Quick Reference

A/B Prompt Testing & Versioning

Move prompts from ad-hoc drafts to production assets with repeatable testing, versioning, and regression safety. Apply this when launching a new LLM feature, when prompt quality degrades after a model update, when multiple team members edit prompts, or when you need evidence before promoting a prompt change to production.

Tools

Prompt Tester — scripts/prompt_tester.py

A/B-evaluates two prompts against a structured test suite. Scores expected-content coverage, forbidden-content violations, regex/format compliance, and length sanity.

python scripts/prompt_tester.py \
  --prompt-a-file prompts/a.txt \
  --prompt-b-file prompts/b.txt \
  --cases-file testcases.json \
  --runner-cmd 'my-llm-cli --prompt {prompt} --input {input}' \
  --format text

Prompt Versioner — scripts/prompt_versioner.py

Tracks prompt history with immutable versions, semantic diffs, and changelogs. Never overwrites past versions.

# Add a version
python scripts/prompt_versioner.py add \
  --name support_classifier \
  --prompt-file prompts/support_v3.txt \
  --author alice

# Diff versions
python scripts/prompt_versioner.py diff --name support_classifier --from-version 2 --to-version 3

# Changelog
python scripts/prompt_versioner.py changelog --name support_classifier

A/B Test & Promotion Workflow

Use this when promoting any prompt edit to production:

1. Baseline — store the current prompt as a versioned baseline.
2. Propose — author a candidate prompt with a clear change rationale.
3. A/B test — run prompt_tester.py against the same realistic, edge-case-rich test cases.
4. Promote — only if the candidate's average score improves and must_not_contain violations stay at zero.
5. Track — feed post-release failure cases back into the test suite.

Test Case Design

Each case in your testcases.json should define:

This makes grading deterministic across prompt variants.

Versioning Policy

• One semantic identifier per prompt feature (support_classifier, ad_copy_shortform).
• Author + change rationale on every revision.
• Never overwrite history.
• Always diff before promoting to production.

Pre-Promotion Checklist

Before swapping a prompt in production, confirm:

• Task intent is explicit and unambiguous.
• Output schema/format is explicit.
• Safety and exclusion constraints are explicit.
• No contradictory instructions.
• No verbosity tokens that don't earn their cost.
• A/B score improves and must_not_contain violation count is zero.

Common Pitfalls

1. Picking prompts from single-case outputs — use a realistic, edge-case-rich suite.
2. Changing prompt and model simultaneously — always isolate variables.
3. Missing forbidden-content checks in evaluation criteria.
4. Editing prompts without version metadata, author, or change rationale.
5. Skipping semantic diffs before deploying a new prompt version.
6. Optimizing one benchmark while harming edge cases — track the full suite.
7. Model swap without rerunning the baseline A/B suite.

Additional References

• references/prompt-templates.md — reusable prompt scaffolds for common tasks.
• references/technique-guide.md — when to use which prompt technique.
• references/evaluation-rubric.md — scoring rubric for prompt-tester output.

Common Commands

# Prompt Analysis
python scripts/prompt_optimizer.py prompt.txt --analyze          # Full analysis
python scripts/prompt_optimizer.py prompt.txt --tokens           # Token count only
python scripts/prompt_optimizer.py prompt.txt --optimize         # Generate optimized version

# A/B Prompt Testing
python scripts/prompt_tester.py --prompt-a-file a.txt --prompt-b-file b.txt --cases-file cases.json
python scripts/prompt_versioner.py add --name my_prompt --prompt-file p.txt --author me
python scripts/prompt_versioner.py diff --name my_prompt --from-version 1 --to-version 2

# RAG Evaluation
python scripts/rag_evaluator.py --contexts ctx.json --questions q.json  # Evaluate
python scripts/rag_evaluator.py --contexts ctx.json --compare baseline  # Compare to baseline

# Agent Development
python scripts/agent_orchestrator.py agent.yaml --validate       # Validate config
python scripts/agent_orchestrator.py agent.yaml --visualize      # Show workflow
python scripts/agent_orchestrator.py agent.yaml --estimate-cost  # Token estimation