Protocol: AI Delegation Framework (V1)

📊 Implications

Immediate takeaway: Stop "chatting" with AI. Start assigning deliverables using the 4-part Handshake: Objective (“definition of done”), Context (constraints/tone), Output Schema (table/JSON), Quality Bar (what to reject).

Strategic implication: Template reuse cuts prompting time from 8 to 3 minutes and reduces re-roll rate by 60%. The upfront "schlep" of defining constraints pays compound dividends.

Key risk: Without a defined Output Schema, the AI defaults to "vibes-based" responses — fluent-sounding but non-actionable. You get essays when you need tables.

1. The Shift to Deterministic Output

Problem: "Chat" interfaces encourage vague querying, leading to non-actionable "vibes" based responses.

Solution: Treat the LLM as a Junior Analyst. Do not ask for opinions; assign specific deliverables with defined schemas.

2. The Handshake Protocol

Every task assignment must satisfy the 4-part Handshake before execution begins.

sequenceDiagram participant User as Architect participant AI as Operator User->>AI: 1. Objective (Definition of Done) User->>AI: 2. Context (Constraints/Tone) User->>AI: 3. Output Schema (JSON/Table) AI->>User: [Confirm Understanding / Ask Clarification] User->>AI: [Execute] AI->>User: [Deliverable]

3. The Task Template (JSON-S)

Use this schema for all complex requests. It forces constraint definition.

📄 TEMPLATE: STD-TASK-BRIEF

1. OBJECTIVE: 
   - [ ] Review Portfolio Copy
   - [ ] Identify 3 weakest claims

2. CONTEXT:
   - Target Audience: Tech Recruiters
   - Tone: Confident, terse, quantitative

3. STEPS:
   - READ input file
   - EXTRACT claims
   - CRITIQUE against "So What?" test
   - REWRITE

4. OUTPUT_FORMAT: 
   | Original | Critique | Proposed Rewrite | Metric |

5. QUALITY_BAR:
   - No buzzwords ("passionate", "innovative")
   - Every rewrite must contain a number.

4. Efficiency Metrics

Adopting this protocol resulted in:

Prompting Time: Reduced from 8m to 3m (Template Reuse).
Re-roll Rate: Reduced by 60% (Clearer initial constraints).

Frequently Asked Questions

What is the AI Delegation Framework?

It's a structured protocol for assigning deterministic tasks to LLMs. Instead of vague conversational queries, you provide a 4-part "Handshake" — Objective, Context, Output Schema, and Quality Bar — that forces the AI to produce actionable work product instead of generic advice.

What is the difference between "Chat" and "Work Product"?

"Chat" is open-ended conversation that produces opinions. "Work Product" is constrained output with defined deliverables. The shift happens when you specify an Output Schema (JSON, table, checklist) and a Quality Bar (e.g., "no buzzwords," "every claim must contain a number").

How does the Task Template reduce re-rolls?

By defining constraints upfront — tone, audience, format, and rejection criteria — you eliminate the ambiguity that causes bad first outputs. This reduced re-roll rate by 60% and prompting time from 8 to 3 minutes in measured use.

AI Delegation Framework (V1)