Building Reliable Pipelines: Same Quality Every Time

Some tasks need the same quality every time. Meeting analysis. Development workflows. Content publishing. You can’t iterate through trial-and-error each time—you need reliable pipelines.

This is different from iterative work. Here you build once, run many times.

When You Need Pipelines

Examples of pipeline work:

• Meeting analysis (5 phases, same structure every time)
• Development workflow (context → implement → test → review → finalize)
• Content publishing (create → polish → validate → publish)
• Research methodology (decompose → sweep → plan → deep dive)

Phased Execution

Break complex tasks into phases. Each phase has a validation gate.

Phase 0: Context Loading
    ↓ Gate: Do we have what we need?
Phase 1: Initial Analysis
    ↓ Gate: Structure correct?
Phase 2: Deep Processing
    ↓ Gate: Quality threshold met?
Phase 3: Output Generation
    ↓ Gate: Format valid?
Phase 4: Finalize

Why phases work:

• Each phase is cognitively contained
• Errors caught early, not cascaded
• Checkpoint recovery if something fails
• Progress visibility

Real Example: Meeting Analysis Pipeline

Phase 0: Participant Context
├── Extract participants from transcript
├── Match to known profiles
├── Load relationship context
└── Gate: All participants identified?

Phase 1: Initial Analysis
├── Categorize content (decisions, actions, discussion)
├── Detect language
├── Split public vs. private topics
└── Gate: Metadata complete?

Phase 2: Insight Extraction
├── Scan for transformation moments
├── Rank by impact
├── Define placement strategy
└── Gate: 4-6 quality insights?

Phase 3: Note Generation
├── Generate main note
├── Place insights in narrative
├── Validate frontmatter
└── Gate: Format valid? Safe to share?

Phase 4: Subtext Generation
├── Private layer only
├── Political dynamics
├── Pattern challenges
└── Gate: No duplication with main?

Phase 5: Profile Updates
├── Suggest updates based on observations
├── User approval
├── Apply to profiles
└── Cleanup working files

Each phase produces a working file. Final artifacts are clean. If Phase 3 fails, I don’t lose Phase 1-2 work.

Validation Gates

Gates are explicit checkpoints. Not “looks good enough”—specific criteria.

Gate types:

MUST (Tier 2) — Blocks progress if failed
├── Frontmatter complete
├── Required fields present
├── Format valid
└── No sensitive data exposed

QUALITY (Tier 3) — Flags but doesn't block
├── Insights placed strategically
├── Story flow coherent
├── Appropriate tone
└── Actionable outputs

Gate implementation:

**Validation Gate (Before Phase 3):**
- [ ] All participants identified
- [ ] Profiles loaded
- [ ] Unknown participants handled
- [ ] Context summary written

If any checkbox fails → Fix before proceeding

Why explicit gates:

• “Good enough” drifts over time
• Different runs get different standards
• No way to debug what went wrong
• AI doesn’t know what you care about unless you tell it

Agent Isolation

For testing and review, isolate agents from context pollution.

Blind Testing

The testing agent doesn’t see implementation code:

Testing Agent receives:
├── Interface signatures
├── Spec/requirements
├── Expected behaviors

Testing Agent does NOT receive:
├── Implementation code
├── Chat context about how it was built
├── Internal details

Why: Prevents confirmation bias. Can’t accidentally verify implementation quirks as “correct.” Tests what it SHOULD do, not what it DOES do.

Blind Review

The review agent doesn’t see why decisions were made:

Review Agent receives:
├── The code (files changed)
├── The spec/requirements
├── Test results

Review Agent does NOT receive:
├── Chat context about decisions
├── Why certain shortcuts were taken
├── Previous discussion

Why: Fresh eyes catch what polluted-context eyes miss. No “well, we discussed this and decided…” excuses.

Few-Shot for Consistency

When format matters, show examples. Don’t explain rules.

Categorize sentiment:

"Love this product!" → positive
"Worst experience ever" → negative
"Package arrived on time" → neutral
"Sure, whatever" → negative
"This exceeded expectations!" → positive

Now categorize:
"Pretty good, I guess"

When to use few-shot:

• Output format must be consistent
• Classification tasks
• Style matching
• Structured data extraction

How many examples:

• 3-5 excellent examples > 10 mediocre
• Cover common cases AND edge cases
• Show the exact input → output structure

Scripts for Hard Constraints

Where prompts aren’t reliable enough, use scripts.

# Example: Constrain emotion categories
VALID_EMOTIONS = ['frustrated', 'stressed', 'tired', 'neutral',
                  'focused', 'accomplished', 'flow', 'euphoric']

def validate_emotion(emotion: str) -> str:
    if emotion.lower() not in VALID_EMOTIONS:
        raise ValueError(f"Invalid emotion: {emotion}")
    return emotion.lower()

# LLM can't invent new categories
# Output space is constrained by code

When to use scripts:

• Deterministic behavior required
• Finite set of valid outputs
• Integration with external systems
• Compliance/audit requirements

The principle: LLMs are like side-effect-free functions. Same context + same parameters = same result. Scripts constrain the parameter space.

Prompt Chains

Complex pipelines use chains of prompts, each building on the last.

Prompt 1: Extract raw data
    ↓ (output becomes input)
Prompt 2: Validate and clean
    ↓ (output becomes input)
Prompt 3: Transform to target format
    ↓ (output becomes input)
Prompt 4: Generate final output

Chain design principles:

• Each prompt does ONE thing well
• Output of N is input of N+1
• Validation between steps
• Failure at step N doesn’t lose steps 1 to N-1

Real Example: Research Pipeline

Phase 1: Decomposition (NO TOOLS)
├── Break query into sub-topics
├── Extract common knowledge
├── Identify challenge points
└── Output: Proposed structure

Phase 2: Quick Sweeps (MAX 3 SEARCHES)
├── Temporal check
├── Challenge sweep
└── Output: Adjusted sub-topics

Phase 3: Plan & Confirm (MANDATORY STOP)
├── Show research plan
├── Estimated scope
└── WAIT for user approval

Phase 4: Deep Dives (ONLY AFTER APPROVAL)
├── Per sub-topic research
├── Parallel searches
└── Output: Raw findings

Phase 5: Documentation
├── Synthesize findings
├── Name contradictions
├── Write to file
└── Output: Final research

The MANDATORY STOP at Phase 3 prevents the AI from going on expensive deep dives without approval. Constraint by process design.

Error Handling

When phases fail:

1. Show which gate criterion failed
2. Show expected vs. actual
3. Fix the issue
4. Re-run validation
5. Only proceed when all criteria pass

Example:

⚠️  Phase 3 Validation Failed:

Criterion: "Closing --- on own line"
Expected: [blank line after frontmatter]
Actual: [no blank line]

Fixing... ✅
Re-validating... ✅ Pass

Proceeding to Phase 4...

No silent failures. No “good enough.” Fix or don’t proceed.

Building Your Own Pipelines

1. Identify repeatable work — What do you do the same way every time?

2. Break into phases — What are the natural checkpoints?

3. Define gates — What MUST be true before proceeding?

4. Add isolation where needed — Testing? Review? Separate context.

5. Use scripts for hard constraints — Where prompts aren’t reliable enough.

6. Iterate the pipeline — The first version won’t be perfect. Measure, adjust.

Key Takeaways

1. Phases contain complexity. One thing at a time, validate, proceed.

2. Gates are explicit. Not “looks good”—specific criteria.

3. Isolation prevents bias. Blind testing, blind review.

4. Few-shot beats rules. Show the format, don’t explain it.

5. Scripts for hard constraints. Where prompts aren’t reliable enough.

6. Chains build on each other. Output of N is input of N+1.

7. Fail explicitly. Show what failed, fix it, re-validate.