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Mario Alexandre
Mario Alexandre

Posted on • Originally published at tokencalc.pro

Claude Prompt Best Practices: The 6-Band Framework

#ai #programming #promptengineering #tutorial

Claude Prompt Best Practices: The 6-Band Framework

By Mario Alexandre
March 21, 2026
sinc-LLM
Prompt Engineering

Claude's Strengths and the 6-Band Framework

Anthropic's Claude models are known for instruction-following, safety awareness, and long-context handling. These strengths make Claude particularly responsive to structured prompting, when you provide clear specifications, Claude follows them precisely.

The sinc-LLM framework was developed using Claude-based multi-agent systems as primary test subjects. All 275 observations in the research paper were collected from Claude-powered agents, making this guide especially relevant for Claude users.

The 6 Bands for Claude

x(t) = Σ x(nT) · sinc((t - nT) / T)

The 6 specification bands, with Claude-specific notes:

  • PERSONA, Claude responds well to specific expertise roles. "You are a distributed systems architect" produces better output than "You are helpful."

  • CONTEXT, Claude's long context window (200K tokens) means you can include extensive context. But more is not always better, band decomposition ensures you include only relevant context.

  • DATA, Claude handles structured data well. Provide data in clear formats (JSON, CSV, markdown tables).

  • CONSTRAINTS, This is where Claude excels. Claude's instruction-following means detailed constraints are followed precisely. Invest 42% of your prompt tokens here.

  • FORMAT, Claude produces consistently formatted output when given explicit format specifications. Use examples of desired output format.

  • TASK, Keep it concise. Claude does not need verbose task descriptions when the other 5 bands are well-specified.

Claude-Specific Optimization Tips

Use XML Tags for Band Separation

Claude responds particularly well to XML-tagged sections. Wrap each band in descriptive tags:

Senior security engineer
Production Kubernetes cluster, 50 services
[pod logs from the last 2 hours]

  • Focus on network policy violations only
  • Do not suggest changes to application code
  • Flag any pod-to-pod communication not in the allowlist Table: Pod | Violation | Severity | Recommendation Audit these logs for network policy violations.

Leverage Claude's Thinking

For complex tasks, add a constraint: "Think step by step before answering, but show only the final output." This uses Claude's extended thinking capability without cluttering the output.

Cost Optimization for Claude API

Claude API pricing makes token efficiency critical for production use. The sinc-LLM framework's 97% token reduction directly translates to 97% cost reduction:

Metric Unstructured Prompt 6-Band Prompt
Input tokens 80,000 2,500
Output quality (SNR) 0.003 0.92
Retry rate ~30% ~2%
Effective cost per task $0.52 $0.016

Resources

Tools and references for Claude prompt optimization:

Transform any prompt into 6 Nyquist-compliant bands

Try sinc-LLM Free

Related Articles

Real sinc-LLM Prompt Example

This is the exact JSON format that sinc-LLM uses. Paste any raw prompt at tokencalc.pro to generate one automatically.

{
"formula": "x(t) = Σ x(nT) · sinc((t - nT) / T)",
"T": "specification-axis",
"fragments": [
{
"n": 0,
"t": "PERSONA",
"x": "You are a Claude API specialist with 2 years of production experience. You provide precise, evidence-based analysis with exact numbers and no hedging."
},
{
"n": 1,
"t": "CONTEXT",
"x": "This analysis is part of a production system where accuracy determines revenue. The sinc-LLM framework identifies 6 specification bands with measured importance weights."
},
{
"n": 2,
"t": "DATA",
"x": "Fragment importance: CONSTRAINTS=42.7%, FORMAT=26.3%, PERSONA=7.0%, CONTEXT=6.3%, DATA=3.8%, TASK=2.8%. SNR formula: 0.588 + 0.267 * G(Z1) * H(Z2) * R(Z3) * G(Z4). Production data: 275 observations, 51 agents."
},
{
"n": 3,
"t": "CONSTRAINTS",
"x": "State facts directly. Never hedge with 'I think' or 'probably'. Use exact numbers for every claim. Do not suggest generic solutions. Every recommendation must be specific and verifiable. Include at least 3 MUST/NEVER rules specific to this task."
},
{
"n": 4,
"t": "FORMAT",
"x": "Lead with the definitive answer. Use structured headers. Tables for comparisons. Numbered lists for sequences. Code blocks for implementations. No trailing summaries."
},
{
"n": 5,
"t": "TASK",
"x": "Optimize a Claude system prompt for a customer support bot using all 6 sinc bands"
}
]
}Install: pip install sinc-llm | GitHub | Paper

Originally published at tokencalc.pro

sinc-LLM applies the Nyquist-Shannon sampling theorem to LLM prompts. Read the spec | pip install sinc-prompt | npm install sinc-prompt

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