CRAFT and sinc-LLM Prompt Fields: A Comparison

CRAFT uses five parts: Context, Role, Action, Format, and Target. This article compares their functions with the sinc-LLM fields, focusing on where to put supplied data and constraints.

What CRAFT Stands For

• C — Context: Background information about the situation
• R — Role: The expert role you give the AI
• A — Action: The exact task you want done
• F — Format: How you want the output laid out
• T — Target: Who will read the output

CRAFT's Strengths

CRAFT has a good design. It keeps Context and Role separate. CO-STAR does not do that well. CRAFT also names a Format step, which is helpful. The Action element is more specific than labels like "Objective" or "Task." Among 5-part frameworks, CRAFT is one of the best.

The Target element makes you think about who will read the output. That shapes the depth, word choice, and structure of the response. A technical report for a CTO reads differently than one for a board of directors, even when the facts are the same.

The Two Missing Dimensions

CRAFT has 5 elements. sinc-LLM has 6 bands. The two missing pieces are DATA and CONSTRAINTS.

Missing: DATA (n=2)

CRAFT has no place for specific inputs. If you ask the LLM to analyze a dataset, read a code file, or compare products, CRAFT makes you cram that into Context. But context and data do different jobs. Context answers "what is the situation?" Data answers "what specific information should you use?" Mixing them makes the prompt less clear.

Missing: CONSTRAINTS (n=3)

This is the biggest gap. CRAFT has no constraints slot. Word limits, banned content, required citations, performance targets, compliance rules, edge cases: none of these have a place in CRAFT. They end up spread across Context, Action, and Format, or they get left out entirely.

When using a CRAFT prompt, state the rules and exclusions that the output must satisfy, then check compliance.

Comparing CRAFT and the Six-Field Format

Compare formats with the same tasks, model settings, and scoring rules. Record usability, constraint compliance, format accuracy, and factual accuracy separately.

CRAFT has an explicit Format field. The sinc-LLM format also gives CONSTRAINTS a separate field for rules and exclusions. Check both requirements in the resulting output.

Mapping CRAFT to sinc-LLM

CRAFT	sinc-LLM
Context	CONTEXT (n=1) — direct mapping
Role	PERSONA (n=0) — direct mapping, but sinc-LLM's PERSONA is broader (includes Target audience awareness)
Action	TASK (n=5) — direct mapping
Format	FORMAT (n=4) — direct mapping
Target	PERSONA (n=0) + CONSTRAINTS (n=3) — audience awareness splits across persona calibration and output constraints
—	DATA (n=2) — missing in CRAFT
—	CONSTRAINTS (n=3) — missing in CRAFT

When CRAFT Is Enough

If you mainly write content prompts, such as blog posts, emails, social media, or marketing copy, CRAFT covers what you need. The Target element helps when you care about the audience. Five elements is also easy for a team to learn and remember.

When You Need sinc-LLM

For technical, data, code, research, or compliance tasks, state the supplied DATA and applicable CONSTRAINTS explicitly. The sinc-LLM format gives each its own field; output checks remain part of the workflow.

Transition Guide

If you use CRAFT now, switching to sinc-LLM is simple:

1. Keep Context. Map it to CONTEXT (n=1).
2. Keep Role. Map it to PERSONA (n=0). Fold your Target audience into it.
3. Keep Action. Map it to TASK (n=5).
4. Keep Format. Map it to FORMAT (n=4).
5. Add DATA (n=2). Move specific inputs out of your Context and put them here.
6. Add CONSTRAINTS (n=3). This is the biggest upgrade. List every limit, requirement, and rule that the output must follow.

{
  "formula": "x(t) = \u03a3 x(nT) \u00b7 sinc((t - nT) / T)",
  "T": "specification-axis",
  "fragments": [
    {"n": 0, "t": "PERSONA", "x": "Expert data scientist with 10 years ML experience"},
    {"n": 1, "t": "CONTEXT", "x": "Building a recommendation engine for an e-commerce platform"},
    {"n": 2, "t": "DATA", "x": "Dataset: 2M user interactions, 50K products, sparse matrix"},
    {"n": 3, "t": "CONSTRAINTS", "x": "Must use collaborative filtering. Latency under 100ms. No PII in logs. Python 3.11+. Must handle cold-start users with content-based fallback"},
    {"n": 4, "t": "FORMAT", "x": "Python module with type hints, docstrings, and pytest tests"},
    {"n": 5, "t": "TASK", "x": "Implement the recommendation engine with train/predict/evaluate methods"}
  ]
}

The article maps CRAFT’s fields into the sinc-LLM six-field format. Try the free tool at sincllm.com to prepare those fields, then review them for the task.

// Keep reading

• What an AI Fallback Path Actually Looks Like in a Production System
• AI Monitoring Checklist: What Every Production System Needs on Every Critical Path
• SLO Design for AI Systems: How to Set Error Budgets That Survive a Vendor Update

// Production AI Engineering

Build AI systems that hold up in production.

sinc-LLM designs, audits, and stabilises production AI infrastructure: from vendor evaluation and cost accountability to incident controls and MCP architecture.

See what we do →