How Goal-Driven Execution Reduces User Clarification Needs in LLM Workflows

Goal-Driven Execution eliminates back-and-forth clarification by converting vague instructions into concrete, verifiable success criteria that LLMs can autonomously validate against.

Goal-Driven Execution (GDE) is a design principle implemented in the multica-ai/andrej-karpathy-skills repository that transforms how large language models handle ambiguous user requests. Instead of requiring users to clarify unclear instructions during implementation, GDE forces the model to define testable outcomes upfront. This approach reduces clarification cycles by converting open-ended tasks into discrete, verifiable steps that the model can check independently.

What Is Goal-Driven Execution?

GDE is a systematic approach that replaces imperative commands with declarative, goal-oriented specifications. According to the project documentation in README.md, GDE requires that every task include explicit verification criteria that define exactly what "done" means. This shifts the burden of precision from the user to the model, which must articulate measurable outcomes before writing any code.

How Goal-Driven Execution Eliminates Ambiguity

GDE reduces clarification needs through four specific mechanisms that address the root causes of ambiguity in LLM interactions.

From Imperative to Declarative Syntax

Traditional imperative prompts like "Fix the authentication system" force the model to guess the user's intent. GDE, as documented in CLAUDE.md, rewrites these into declarative checklists with measurable outcomes. For example, "Fix the authentication system" becomes "Write a test that reproduces the session invalidation bug, then make that test pass." This transformation eliminates the need for the model to ask "What exactly should I fix?"

Explicit Success Criteria and Verify Clauses

Each sub-task in a GDE workflow includes a verify clause that specifies exactly how to validate completion. As shown in EXAMPLES.md, these clauses use concrete metrics like "Test passes," "Coverage stays above 80%," or "Response returns HTTP 429 after 10 requests." The model can run these verifications automatically, removing the need for users to confirm "Is this the right approach?" or "Did you finish the task?"

Autonomous Iterative Looping

GDE implements a tight plan → implement → verify loop. If verification fails, the model revisits the same step rather than proceeding to the next task or asking the user for guidance. The skills/karpathy-guidelines/SKILL.md file emphasizes that this autonomous looping prevents the "What did I miss?" clarification requests that typically occur when models assume completion without validation.

Surface-Level Definition of "Done"

By forcing the model to surface the exact condition that defines completion, GDE eliminates hidden assumptions. The model no longer has to guess the user's unstated expectations—such as error handling requirements, performance thresholds, or edge case coverage—because these are explicitly encoded in the verification criteria. This addresses the primary source of post-implementation clarification requests.

Practical Implementation in Code

The EXAMPLES.md file provides concrete before-and-after comparisons showing how GDE transforms vague requests into verifiable workflows.

Vague Request vs. Verifiable Goals:

User request (vague):
Fix the authentication system

Goal-Driven Execution (verifiable):
1️⃣ Write a failing test: change password → old session should be invalidated  
   ✅ Verify: test fails (bug reproduced)  

2️⃣ Implement session invalidation on password change  
   ✅ Verify: test now passes  

3️⃣ Add edge-case tests (multiple sessions, concurrent changes)  
   ✅ Verify: all new tests pass  

4️⃣ Run full test suite to ensure no regression  
   ✅ Verify: all existing tests remain green

Multi-Step API Development:

User request (all at once):
Add rate limiting to the API

Goal-Driven Execution (step-by-step):
1️⃣ Implement basic in-memory limiter for a single endpoint  
   ✅ Verify: 100 requests → first 10 succeed, rest return 429  

2️⃣ Refactor as middleware for all endpoints  
   ✅ Verify: same rate-limit behavior on /users and /posts; existing tests still pass  

3️⃣ Swap to Redis backend for distributed limits  
   ✅ Verify: limit persists across restarts and across two app instances  

4️⃣ Expose configurable rates per endpoint  
   ✅ Verify: /search allows 10/min, /users allows 100/min; config parsing succeeds

Key Source Files and Guidelines

The GDE methodology is documented across several files in the multica-ai/andrej-karpathy-skills repository:

• README.md – Defines the core GDE principle and explains how to recognize when it is working effectively
• CLAUDE.md – Provides formal guidelines for writing success criteria and structuring verify clauses
• EXAMPLES.md – Contains concrete before-and-after examples comparing vague requests to verifiable workflows
• skills/karpathy-guidelines/SKILL.md – Mirrors the GDE guidelines for integration with Cursor and other AI coding assistants

Summary

Goal-Driven Execution reduces user clarification needs by transforming ambiguous natural language instructions into concrete, testable objectives. Key takeaways include:

• Declarative specifications replace imperative commands, eliminating guesswork about user intent
• Explicit verify clauses provide machine-checkable success criteria that remove the need for manual confirmation
• Autonomous iteration loops allow the model to self-correct without user intervention when verification fails
• Surface-level "done" definitions prevent hidden assumptions that typically trigger post-implementation clarification requests

Frequently Asked Questions

What is the difference between Goal-Driven Execution and standard prompt engineering?

Standard prompt engineering focuses on crafting better inputs to elicit desired outputs in a single pass. Goal-Driven Execution, as defined in CLAUDE.md, structures the entire workflow as a series of verifiable milestones with explicit success criteria. While prompt engineering asks the model to guess intent, GDE forces the model to define measurable outcomes before implementation begins.

How does Goal-Driven Execution handle situations where verification fails?

When verification fails, the GDE workflow triggers an autonomous iteration loop. According to the implementation guidelines in skills/karpathy-guidelines/SKILL.md, the model must revisit the current step, adjust the implementation, and rerun the verification without asking the user for guidance. This prevents the model from proceeding with broken assumptions or requesting clarification about what went wrong.

Can Goal-Driven Execution be applied to non-coding tasks?

Yes, the principles documented in README.md apply to any task where success can be defined objectively. For documentation tasks, verification might mean "all links resolve" or "readability score exceeds 60." For data analysis, it could mean "statistical test returns p < 0.05." The key requirement is that the verification clause must be binary and objectively testable, regardless of the domain.

What makes a good verify clause in Goal-Driven Execution?

A good verify clause, as shown in EXAMPLES.md, must be specific, automatic, and binary. It should reference concrete metrics like "test passes," "coverage stays above 80%," or "response returns HTTP 429." Avoid subjective criteria like "looks good" or "works better" that require human judgment. The clause must be something the model can check programmatically without user intervention.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}