ChatGPT: crafting optimized prompts for highly specialized tasks
- Graziano Stefanelli
- Aug 29
- 4 min read
Deliverables improve when prompts are clear, structured, and grounded. Below is a practical playbook—rooted in the current platform features—for building prompts that produce precise, repeatable, and machine-readable results at scale.
Put roles, scope, and rules in the right place.
A conversation works best when intent and constraints live in the control message, while data lives in the user content.
Role & policy belong in the system/developer message: what the model is, what it must do, and what it must never do.
Task details and examples go in the user message, fenced with clear delimiters like """ ... """ or XML blocks.
Data and instructions should never be mixed; keep them in separate fenced sections.
Mini-template.
System/Developer:
- You write concise, source-grounded answers.
- Follow JSON schema strictly. No extra keys.
User:
INSTRUCTIONS:
"""
Goal: Extract medication regimens from patient notes.
Style: Return JSON only. No prose.
"""
DATA:
"""
[paste notes or records here]
"""
Use structured outputs to lock the shape of the answer.
For specialized work—extraction, classification, scoring—returning schema-valid JSON is the fastest way to make results consumable.
Enable Structured Outputs and provide a JSON Schema.
Turn on strict validation so ill-formed replies are retried or rejected.
Keep schemas small and shallow (few properties, ≤3 nested levels) for robustness.
Schema example (concise).
{
"type": "object",
"properties": {
"patient_id": {"type": "string"},
"medications": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string"},
"dose": {"type": "string"},
"schedule": {"type": "string"},
"evidence_snippet": {"type": "string"}
},
"required": ["name","evidence_snippet"]
}
}
},
"required": ["patient_id","medications"]
}
Why this matters. Machine-readable outputs cut manual cleanup, support automated QA, and enable downstream analytics without custom parsers.
Force the right tools—and run them in parallel when possible.
Complex tasks often require search, file handling, code execution, or other tools.
Define tools/functions with precise parameter types (units, enums, regex-like hints).
Use tool_choice to require a tool when the step demands it.
Enable parallel tool calls for independent subtasks to reduce latency.
Keep tool descriptions concrete: inputs, outputs, and error shapes.
Tool definition sketch.
{
"type": "function",
"function": {
"name": "fetch_10k",
"description": "Download and parse a company's latest 10-K.",
"parameters": {
"type":"object",
"properties": {
"ticker":{"type":"string","pattern":"^[A-Z]{1,5}$"},
"year":{"type":"integer","minimum":2009,"maximum":2025}
},
"required":["ticker","year"]
}
}
}
Provide compact examples, not chain-of-thought.
Examples teach formatting and edge-case handling without exposing hidden reasoning.
Add 2–3 few-shot examples that exactly match your target format.
Avoid verbose “think step by step” prompts.
If justification is required, ask for a short rationale (e.g., 2–3 bullets) or a checklist.
Few-shot block.
EXAMPLE 1
Input: "Ibuprofen 400 mg BID..."
Output: {"name":"ibuprofen","dose":"400 mg","schedule":"BID","evidence_snippet":"Ibuprofen 400 mg BID ..."}
EXAMPLE 2
Input: "Metformin 500 mg once daily..."
Output: {"name":"metformin","dose":"500 mg","schedule":"QD","evidence_snippet":"Metformin 500 mg once daily ..."}
Control length and boundaries precisely.
Specialized tasks often require fixed-size outputs and predictable truncation.
Set max_output_tokens to cap verbosity.
Provide stop sequences (e.g., \n###) to terminate exactly when the payload is complete.
For deterministic lengths (“exactly 5 bullets”), combine a clear instruction with a token cap and a bullet template.
Length-bounded pattern.
Return exactly 5 bullets, each ≤ 18 words.
Stop generating when you output "###END".
Stream, cache, and batch for real workloads.
When moving from prompt craft to production, throughput and cost matter.
Use the Responses API to unify text, tools, and structured outputs, with streaming for partial tokens.
Turn on prompt caching for large, repeated system prompts.
Use the Batch API for high-volume document runs; parallelize independent items.
Keep latency goals per stage (ingest, retrieve, synthesize) in your telemetry.
Operational table.
Evaluate before shipping with graders and rubrics.
Reliability means testing prompts like software.
Build a rubric (JSON) that encodes pass/fail criteria and weights.
Score outputs with a grader endpoint against references or heuristics.
Track precision/recall, format validity, and failure modes (missing required keys, empty arrays, long strings).
Rubric sketch.
{
"criteria": [
{"name":"schema_valid","weight":0.4},
{"name":"evidence_present","weight":0.3},
{"name":"field_accuracy","weight":0.3}
],
"threshold": 0.85
}
Ready-to-use patterns for niche problems.
Schema-locked extraction.When: clinical notes, product specs, legal clauses.
How: strict JSON schema; examples show tricky units; require evidence_snippet for every field.
Tool-first retrieval.When: finance filings, case law, scientific PDFs.
How: force the search/file tool; parse → normalize → summarize; attach source URLs with offsets.
Length-bounded abstracts.When: medical/academic abstracts or UX microcopy.
How: exact bullet count; max_output_tokens; stop at ###END; reject prose outside bullets.
Realtime assistants.When: voice or live dashboards.
How: stream tokens; interleave tool calls; return JSON deltas for UI updates.
One end-to-end template that covers it all.
System/Developer:
- Role: Domain summarizer that returns JSON only.
- Obey the JSON schema strictly. Do not add keys.
User:
INSTRUCTIONS:
"""
Task: Extract key safety findings from the study text.
Output: Valid JSON as per schema. Include evidence_snippet for each claim.
Length: Keep each snippet ≤ 40 words.
"""
SCHEMA:
"""
{ ... paste schema from above ... }
"""
EXAMPLES:
"""
Input: "... sample text ..."
Output: { "patient_id":"A12", "medications":[...] }
"""
DATA:
"""
... paste study text here ...
"""
CONSTRAINTS:
- Use only information present in DATA.
- If uncertain, leave the field out; do not fabricate.
- End your output with "###END".
Why this works. Clear roles, delimited instructions, few-shot examples, schema-locked outputs, tool forcing (when needed), and length controls produce answers that are auditable, consistent, and ready for automation—exactly what specialized tasks demand.
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