Senior Prompt Engineer
Eval-driven prompt engineering, RAG quality measurement, and agent workflow validation. Everything here is model-agnostic by design: techniques are framed by what they do, not by which model generation they were observed on, and the tools never hardcode model IDs or pricing — you supply your provider's current rates when you want dollar figures.
Operating Rules
- Never change a prompt without a baseline. Capture metrics first (
--analyze --output baseline.json), then compare every iteration against it.
- Eval set before optimization. 10–20 representative cases with expected outputs minimum. If the user has no eval set, build one with them before touching the prompt — optimizing against vibes is the #1 failure mode.
- Prefer platform features over prompt hacks. If the provider offers native structured outputs / JSON schema enforcement, tool-use APIs, or prompt caching, use those instead of "respond ONLY with JSON" incantations. Prompt-level format enforcement is the fallback, not the default.
- Current-generation models need less scaffolding. Don't add chain-of-thought boilerplate, role framing, or few-shot examples reflexively — frontier models often do worse with redundant scaffolding. Add each element only when the eval set shows it helps.
- Cost numbers are always user-supplied. Look up the provider's current per-Mtok pricing and pass it via
--price-per-mtok (never trust a cached price table — including any you remember).
Tools (exact CLIs, all stdlib)
1. Prompt Optimizer — scripts/prompt_optimizer.py
Static analysis: token estimate, clarity/structure scores (0–100), ambiguity + redundancy detection, few-shot example extraction.
# Full analysis (human-readable report)
python3 scripts/prompt_optimizer.py prompt.txt --analyze
# Save machine-readable baseline for later comparison
python3 scripts/prompt_optimizer.py prompt.txt --analyze --json --output baseline.json
# Token estimate; cost only if you supply your provider's current rate
python3 scripts/prompt_optimizer.py prompt.txt --tokens --model claude --price-per-mtok 3.00
# Whitespace/redundancy-trimmed version
python3 scripts/prompt_optimizer.py prompt.txt --optimize --output optimized.txt
# Extract Input/Output few-shot pairs to JSON
python3 scripts/prompt_optimizer.py prompt.txt --extract-examples --output examples.json
# Compare a revision against the saved baseline
python3 scripts/prompt_optimizer.py optimized.txt --analyze --compare baseline.json
--model accepts any string; only the tokenizer family is inferred (names containing "claude" → 3.5 chars/token, otherwise 4.0). Exit 0 on success, 1 on missing file.
2. RAG Evaluator — scripts/rag_evaluator.py
Measures retrieval and grounding quality from two JSON files (formats printed in --help).
python3 scripts/rag_evaluator.py --contexts retrieved.json --questions eval_set.json
python3 scripts/rag_evaluator.py --contexts ctx.json --questions q.json --k 10 --json
python3 scripts/rag_evaluator.py --contexts ctx.json --questions q.json --output report.json --verbose
python3 scripts/rag_evaluator.py --contexts ctx.json --questions q.json --compare baseline_report.json
Reports context relevance, precision@k, coverage, answer faithfulness, groundedness. Treat relevance < 0.80 as a retrieval problem (chunking/embedding/filtering), not a prompt problem — fix retrieval before rewriting the generation prompt.
3. Agent Orchestrator — scripts/agent_orchestrator.py
Validates agent configs (YAML/JSON): tool wiring, missing required config, loop risk, token estimates.
python3 scripts/agent_orchestrator.py agent.yaml --validate
python3 scripts/agent_orchestrator.py agent.yaml --visualize --format mermaid
python3 scripts/agent_orchestrator.py agent.yaml --estimate-cost --runs 100 \
--input-price-per-mtok 3.00 --output-price-per-mtok 15.00
Without the two price flags, --estimate-cost reports token estimates only. The model: field in the config is informational — any model name is accepted.
Workflows
Prompt Optimization (eval-gated)
- Baseline:
python3 scripts/prompt_optimizer.py current_prompt.txt --analyze --json --output baseline.json
- Diagnose from the report: ambiguous verbs ("analyze", "handle"), redundant blocks, missing output contract, token waste.
- Apply one change at a time, in this order of leverage:
| Symptom | Fix |
|---|
| Malformed/unparseable output | Native structured outputs / JSON schema if the API supports it; explicit schema-in-prompt otherwise |
| Inconsistent answers across runs | Tighten instructions + add 2–3 contrastive examples (one near-miss showing what NOT to do) |
| Misses edge cases | Enumerate the edge cases explicitly; add a "when uncertain, do X" rule |
| Token bloat on repeated calls | Move stable prefix (system rules, examples) first so prompt caching applies; trim redundancy |
| Wrong reasoning on hard cases | Ask for stepwise reasoning in a scratch field the consumer ignores, or use the provider's extended-thinking mode |
- Re-analyze and compare:
python3 scripts/prompt_optimizer.py revised.txt --analyze --compare baseline.json
- Eval gate (must pass before shipping): run the revised prompt over the eval set, write per-case pass/fail to
eval_results.json, then assert:
python3 scripts/prompt_optimizer.py revised.txt --analyze --json --output revised.json \
&& python3 -c "
import json, sys
r = json.load(open('revised.json')); b = json.load(open('baseline.json'))
ok = r['clarity_score'] >= b['clarity_score'] and r['token_count'] <= b['token_count'] * 1.10
sys.exit(0 if ok else 1)"
echo "gate exit=$?" # 0 = ship; 1 = regression, iterate again
Pair this structural gate with your task-level eval: the revision must not lose any previously-passing eval case (no-regression rule).
Few-Shot Example Design
- Define the task contract first (input shape, output shape, edge-case policy).
- Start with zero examples and measure — current models often need none. Add examples only for failure clusters the eval reveals.
- When adding: 3–5 max, ordered simple → edge → negative (what NOT to extract), formatted identically to the real output contract.
- Validate consistency:
python3 scripts/prompt_optimizer.py prompt_with_examples.txt --extract-examples --output examples.json and inspect that every extracted pair parses against your schema.
- Re-run the eval set; if a case passes only because it resembles an example, add a held-out variant to the eval set.
Structured Output Design
- Write the JSON Schema first (types, enums, required, maxLength).
- Prefer API-native enforcement: structured-outputs / response-schema / tool-call parameters guarantee shape; prompt text cannot.
- Fallback (API without schema support): include the schema rendered as field-by-field rules + one valid example, and instruct "output only the JSON object".
- Gate: pipe 10 eval outputs through a schema validator (
python3 -c "import json,sys; [json.loads(l) for l in sys.stdin]" at minimum); 10/10 must parse, else return to step 2.
RAG Tuning Loop
- Build
questions.json (id, question, reference answer) and capture current retrievals to contexts.json.
python3 scripts/rag_evaluator.py --contexts contexts.json --questions questions.json --output rag_baseline.json- Fix the lowest metric first: relevance → chunking/embeddings/metadata filters; faithfulness → grounding instructions + "answer only from context" + citation requirement; coverage → retrieval k / query expansion.
- Gate:
python3 scripts/rag_evaluator.py --contexts new_contexts.json --questions questions.json --compare rag_baseline.json — every metric must be ≥ baseline; any regression blocks the change.
Agent Config Review
python3 scripts/agent_orchestrator.py agent.yaml --validate — must exit with VALIDATION PASSED; fix every error and warning (missing tool config, unbounded iterations, loop risk).- Check context discipline: each tool description ≤ 1–2 sentences, tool count minimal for the job, stable system prompt placed first (cache-friendly), iteration cap + early-exit condition present.
- Budget:
--estimate-cost --runs N with your current prices; if cost/run exceeds budget, cut tools or context before downgrading the model.
References
| File | Contains | Load when user asks about |
|---|
references/prompt_engineering_patterns.md | 10 prompt patterns with input/output examples | "which pattern?", few-shot design, decomposition, meta-prompting |
references/llm_evaluation_frameworks.md | Eval metrics, scoring methods, A/B testing | "how to evaluate?", "measure quality", "compare prompts" |
references/agentic_system_design.md | Agent architectures (ReAct, Plan-Execute, Tool Use) | "build agent", "tool calling", "multi-agent" |
Related Skills
engineering-team/skills/senior-ml-engineer — model deployment and serving (this skill stops at the prompt/eval layer)engineering/rag-architect — RAG system architecture (this skill measures RAG quality; that one designs the pipeline)engineering/agent-designer — full agent system design (this skill validates configs; that one designs the architecture)