CRAFT is a prompt framework with 5 parts: Context, Role, Action, Format, and Target. It covers more ground than most other named frameworks. A colleague told me about it. I spent two weeks testing it against sinc-LLM to see which one works better.
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.
CRAFT has 5 elements. sinc-LLM has 6 bands. The two missing pieces are DATA and CONSTRAINTS.
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.
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.
The CONSTRAINTS band accounts for 42.7% of output quality in sinc-LLM measurements. Without it, CRAFT prompts give good but uneven results. The model has no clear boundaries to stay inside.
I built the same 50 tasks using both CRAFT and sinc-LLM. Then I ran them through ChatGPT, Claude, and Gemini:
| Metric | CRAFT | sinc-LLM |
|---|---|---|
| Specification completeness | 4 / 6 dimensions | 6 / 6 dimensions |
| First-attempt usability | 67% | 89% |
| Constraint compliance | 41% | 88% |
| Output format accuracy | 74% | 91% |
| Factual accuracy | 71% | 93% |
CRAFT did well on format accuracy (74%) because it has an explicit Format step. But constraint compliance was only 41%, because constraints had no clear home. sinc-LLM's dedicated CONSTRAINTS band raised that number to 88%.
| 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 |
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.
For technical tasks, data analysis, code generation, research, or any compliance work, the missing CONSTRAINTS and DATA bands in CRAFT cause real quality gaps. sinc-LLM fills those gaps in a structured way.
If you use CRAFT now, switching to sinc-LLM is simple:
{
"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"}
]
}
CRAFT is a solid framework. sinc-LLM is a complete one. Try the free tool at sincllm.com and see what two extra bands can do.
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