Claude Prompt Best Practices: The 6-Band Framework

Claude's Strengths and the 6-Band Framework

Anthropic's Claude models are good at following instructions, staying safe, and reading long text. These traits make Claude easy to guide. When you give Claude clear rules, it follows them closely.

I built the sinc-LLM framework using Claude-based multi-agent systems as the main test subjects. All 275 observations in my research paper came from Claude-powered agents. That makes this guide very useful for Claude users.

The 6 Bands for Claude

Here are the 6 specification bands, with Claude-specific notes from my testing:

1. PERSONA, Claude does well when you give it a clear role. "You are a distributed systems architect" gets better results than "You are helpful."
2. CONTEXT, Claude can read up to 200K tokens at once. That is a huge window. But more is not always better. Band decomposition helps you include only the context that matters.
3. DATA, Claude handles structured data well. Give your data in clean formats like JSON, CSV, or markdown tables.
4. CONSTRAINTS, This is where Claude shines. Claude follows detailed rules precisely. Put 42% of your prompt tokens here.
5. FORMAT, Claude gives neat, consistent output when you show it exactly what format you want. Use a sample of the output you expect.
6. TASK, Keep it short. Claude does not need a long task description when the other 5 bands are set up well.

Claude-Specific Optimization Tips

Use XML Tags for Band Separation

Claude works especially well with XML tags around each section. Wrap each band in clear tags like this:

<persona>Senior security engineer</persona>
<context>Production Kubernetes cluster, 50 services</context>
<data>[pod logs from the last 2 hours]</data>
<constraints>
- Focus on network policy violations only
- Do not suggest changes to application code
- Flag any pod-to-pod communication not in the allowlist
</constraints>
<format>Table: Pod | Violation | Severity | Recommendation</format>
<task>Audit these logs for network policy violations.</task>

Leverage Claude's Thinking

For complex tasks, add this rule: "Think step by step before answering, but show only the final output." This turns on Claude's extended thinking mode without adding clutter to the result.

Cost Optimization for Claude API

Claude API pricing means that using fewer tokens saves real money in production. My sinc-LLM framework cuts token use by 97%. That means costs drop by 97% too:

Resources

These are the tools and references I suggest for Claude prompt work:

• sinc-LLM Transformer, Splits any prompt into 6 bands automatically
• GitHub Repository, Open source framework
• Research Paper, Full method with 275 observations
• Universal Prompt Template, Works with Claude too
• Constraints Guide, The 42.7% quality factor

{
  "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"
    }
  ]
}