Why Chaining Works
When you try to do everything in one prompt, the AI gets overwhelmed and misses details. Breaking tasks into steps gives you:- Better accuracy because the AI focuses on one thing at a time
- Visibility into the AI’s thinking at each stage
- The ability to correct course if something goes wrong
- Reusable workflows you can apply to similar documents
The Basic Approach
After each AI response, you make a decision: continue to the next step, refine what you got, or change direction entirely. You’re in control at every stage.Three Core Techniques
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Sequential Chain
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Forked Chain
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Loop and Refine
Common Legal Chains
Clause Review
- Extract the relevant clause
- Summarize what it says
- Identify the risks
- Suggest better language
Risk Analysis
- List all potential risks
- Rank them by importance
- Explain why each matters
- Suggest how to fix them
Negotiation Prep
- Find the problematic terms
- Group by how hard to negotiate
- Create fallback positions
- Format for management review
Building Your Own Chains
The Standard Template
Most legal review chains follow this pattern:Making Chains Work Better
- Keep steps focused. Each prompt should do one clear thing.
- Reference previous steps explicitly. Say “Using the risks you just identified…” not just “Using the risks…”
- Label your steps. Use “Step 1:” or ”## Analysis Phase” to keep things organized.
- Save successful chains. When a chain works well, save it for next time.
- Start fresh if things go wrong. If the AI gets confused, don’t try to fix it. Begin again with clearer instructions.
- Get reasoning before edits. First understand the problems, then work on solutions.
When to Use Chains
Use Chains For:
- Complex documents with multiple issues
- Tasks where you need to see the thinking process
- Situations where you might need to adjust your approach
- Work requiring multiple perspectives
- Anything you’d normally do in stages yourself
Skip Chains For:
- Simple, routine tasks
- Standard playbook applications
- Quick yes/no questions
- When you know exactly what output you need
Common Problems
- The chain drifts off topic: Start over with clearer first step.
- The AI forgets earlier context: Begin each step with “Based on the above…” or similar reminder.
- Quality varies between steps: Add a quick verification between major stages.
- Too many steps: If you need more than 5-6 steps, you might be overcomplicating. Combine related steps.