Project 6: Long-Context Evaluation Harness

Build a deterministic evaluation harness that measures memory reliability when key evidence moves across long-context positions.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 20-40 hours
Main Programming Language Python (Alternatives: TypeScript, Go)
Alternative Programming Languages TypeScript, Go
Coolness Level Level 7
Business Potential Level 4
Prerequisites Projects 1-4, evaluation mindset
Key Topics lost-in-the-middle testing, prompt zoning, regression gating

1. Learning Objectives

By completing this project, you will:

  1. Build controlled long-context test suites with known answer spans.
  2. Measure quality across evidence-position buckets.
  3. Distinguish retrieval failures from generation failures.
  4. Define release gates for long-context reliability.

2. All Theory Needed (Per-Concept Breakdown)

2.1 Position Sensitivity and Reliability Gating

Fundamentals Long-context capacity does not guarantee long-context reliability. Models can underuse important information depending on where it appears in the prompt. A reliability harness makes this behavior measurable by placing the same evidence in different positions and scoring output faithfulness.

Deep Dive into the concept Most teams evaluate RAG systems with aggregate metrics and short prompts, then assume long-context behavior scales smoothly. In practice, reliability can degrade as contexts become long and noisy. Position sensitivity means a fact placed near beginning or end may be used more effectively than the same fact buried in the middle. If you do not test this explicitly, regressions remain hidden until users report inconsistent behavior.

A robust harness uses deterministic fixtures. Each case contains a query, a canonical supporting passage, distractor passages, and expected answer/citation criteria. By relocating the supporting passage across position buckets (early, mid, late), you can quantify where failures cluster. Keep distractor noise controlled so results remain interpretable.

You should separate stage metrics. Retrieval correctness asks whether the right chunk was selected. Generation faithfulness asks whether the final answer is supported by that chunk. A case can pass retrieval and fail generation if evidence is poorly placed or diluted. This stage separation is critical for diagnosis.

Prompt zoning is a common mitigation strategy. Fixed zones for policy, intent, and evidence reduce accidental burial of key facts. The harness should compare zoned vs unstructured assembly to measure impact. If zoned prompts improve middle-bucket performance, you have objective evidence for architecture changes.

Reliability gating turns evaluation into release policy. Define bucket-specific thresholds, not only global averages. For example, global faithfulness may look acceptable while middle bucket collapses. Release gates should block deployment when any critical bucket drops below threshold.

Dataset realism matters. Synthetic data is useful for determinism but may miss domain complexity. Combine synthetic control suites with sampled real queries that have curated expected evidence. Track both to balance internal validity and external validity.

Finally, create a feedback loop. Every production incident involving missed context should become a new fixture. Over time, your harness becomes a living regression set reflecting real failure patterns. This is how memory systems mature beyond ad hoc debugging.

How this fits on projects

  • Primary here.
  • Feeds release readiness for Project 4.

Definitions & key terms

  • Position bucket: evidence placement category in prompt.
  • Lost-in-the-middle effect: reduced usage of mid-context evidence.
  • Reliability gate: threshold rule that blocks release on regressions.
  • Fixture drift: test data changes that break comparability.

Mental model diagram (ASCII)

same evidence chunk E

Case A: [E .... noise ....]  -> score A
Case B: [noise .. E .. noise] -> score B
Case C: [.... noise .... E]   -> score C

compare A, B, C to detect position sensitivity

How it works (step-by-step)

  1. Build deterministic fixtures with known answer support.
  2. Generate position variants for each fixture.
  3. Run retrieval + generation pipeline.
  4. Score retrieval correctness and faithfulness by bucket.
  5. Compare to release thresholds.
  6. Emit alert/report.

Invariants:

  • Fixture IDs and expected outputs are versioned.
  • Same model/settings for bucket comparisons.
  • Bucket-level scores are stored independently.

Failure modes:

  • Aggregate-only metrics hiding bucket collapse.
  • Non-deterministic fixture generation.
  • Retrieval and generation errors mixed together.

Minimal concrete example

fixture_id=LM-042
begin_bucket faithfulness=0.90
middle_bucket faithfulness=0.62
end_bucket faithfulness=0.88
release_gate => FAIL (middle < 0.70)

Common misconceptions

  • “Large context means problem solved.”
  • “One faithfulness score is enough.”
  • “Synthetic suites are useless.” (They are essential for controlled tests.)

Check-your-understanding questions

  1. Why must bucket scores be tracked separately?
  2. How do you isolate retrieval vs generation failures?
  3. Why should incidents feed back into fixtures?

Check-your-understanding answers

  1. To detect localized reliability failures hidden in global averages.
  2. Score retrieval correctness before generation faithfulness.
  3. To prevent repeat regressions from known real-world failure modes.

Real-world applications

  • Release gating for enterprise copilots.
  • Long-document question answering reliability testing.

Where you’ll apply it

  • This project directly.
  • Also improves: Project 4.

References

  • Lost in the Middle: https://arxiv.org/abs/2307.03172

Key insights Memory reliability must be measured by where evidence is placed, not just whether it exists.

Summary You create the final quality gate that turns memory architecture into an engineering discipline.

Homework/Exercises to practice the concept

  1. Build 10 fixtures and generate begin/middle/end variants.
  2. Define release thresholds for each bucket.

Solutions to the homework/exercises

  1. Keep expected evidence IDs fixed across variants.
  2. Use strict minimum for middle bucket to avoid hidden regressions.

3. Project Specification

3.1 What You Will Build

An evaluation harness that:

  • executes long-context fixtures,
  • tracks retrieval and faithfulness metrics by position bucket,
  • produces release pass/fail decisions.

3.2 Functional Requirements

  1. Fixture loader with schema validation.
  2. Position-variant generator.
  3. Stage-wise scoring (retrieval + generation).
  4. Bucket-level threshold gating.
  5. Report exporter with historical comparisons.

3.3 Non-Functional Requirements

  • Performance: full suite run under 20 minutes.
  • Reliability: deterministic results on fixed fixtures.
  • Usability: clear root-cause hints in reports.

3.4 Example Usage / Output

$ llm-memory longctx-eval run --suite fixtures/lost_middle_v2.json
begin=0.88 middle=0.63 end=0.85
gate=FAIL reason="middle bucket below threshold"

3.5 Data Formats / Schemas / Protocols

fixture:
- fixture_id
- query
- canonical_evidence_chunk_id
- distractor_chunk_ids
- expected_answer_pattern

3.6 Edge Cases

  • Multiple evidence chunks required for one answer.
  • Ambiguous query with valid alternate wording.
  • Retrieval returns equivalent but differently chunked evidence.

3.7 Real World Outcome

3.7.1 How to Run (Copy/Paste)

$ llm-memory longctx-eval run --suite fixtures/lost_middle_v2.json
$ llm-memory longctx-eval compare --current reports/run-2026-02-11.json --baseline reports/run-2026-02-01.json

3.7.2 Golden Path Demo (Deterministic)

$ llm-memory longctx-eval run --suite fixtures/golden_position_suite.json
[RESULT] begin=0.89 middle=0.74 end=0.87 gate=PASS
exit_code=0

3.7.3 Failure Demo (Deterministic)

$ llm-memory longctx-eval run --suite fixtures/regression_suite.json
[RESULT] begin=0.86 middle=0.61 end=0.84 gate=FAIL
exit_code=6

4. Solution Architecture

4.1 High-Level Design

fixture loader -> variant generator -> pipeline runner -> scorers -> gate engine -> reporter

4.2 Key Components

Component Responsibility Key Decisions
Variant Generator place evidence across buckets deterministic positioning
Pipeline Runner call retrieval+generation stable settings
Scorers retrieval and faithfulness metrics stage separation
Gate Engine pass/fail decision bucket-level thresholds

4.3 Data Structures (No Full Code)

BucketScore{bucket,retrieval_score,faithfulness_score,cases}
GateResult{status,failed_buckets,recommendations}

4.4 Algorithm Overview

  1. Load and validate fixtures.
  2. Generate position variants.
  3. Execute full pipeline per variant.
  4. Compute bucket metrics.
  5. Apply release thresholds.

Complexity:

  • Time: O(fixtures * buckets * pipeline_cost).
  • Space: O(fixtures + reports).

5. Implementation Guide

5.1 Development Environment Setup

# prepare fixture suite, run baseline, set threshold config

5.2 Project Structure

p06-long-context-eval/
  src/
    fixture_loader
    variant_generator
    runner
    scorers
    gate_engine
  fixtures/
  reports/

5.3 The Core Question You’re Answering

“Can my memory pipeline stay reliable when relevant evidence is hard to attend in long contexts?”

5.4 Concepts You Must Understand First

  • Position sensitivity.
  • Stage-wise failure attribution.
  • Regression gating policy.

5.5 Questions to Guide Your Design

  • Which bucket thresholds are release blockers?
  • How much regression is acceptable before rollback?

5.6 Thinking Exercise

Predict metric changes when you move high-value evidence from early to middle bucket for 15 fixtures.

5.7 The Interview Questions They’ll Ask

  1. How do you test lost-in-the-middle effects?
  2. How do you separate retrieval and generation regressions?
  3. What release gates would you implement for RAG reliability?
  4. How do you keep evaluation deterministic?
  5. How do you evolve fixtures over time?

5.8 Hints in Layers

  • Hint 1: start with fixed deterministic fixtures.
  • Hint 2: score retrieval and generation separately.
  • Hint 3: gate on bucket-level thresholds.
  • Hint 4: store reports for trend analysis.

5.9 Books That Will Help

Topic Book Chapter
Evaluation discipline Code Complete Testing strategy
Architecture gating Fundamentals of Software Architecture Quality attributes

5.10 Implementation Phases

  • Phase 1: fixtures + variant generation.
  • Phase 2: scorers + bucket metrics.
  • Phase 3: release gate engine + report comparisons.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
gate granularity global-only / bucket-level bucket-level catches hidden regressions
fixture source synthetic-only / mixed mixed realism + control

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit scorer correctness known fixture outputs
Integration full eval pipeline suite run with reports
Regression release gating baseline vs current diff

6.2 Critical Test Cases

  1. Middle bucket below threshold triggers fail.
  2. Retrieval pass + generation fail case classification.
  3. Fixture schema violation causes validation error.

6.3 Test Data

Versioned fixture packs with deterministic expected answer patterns.

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
Aggregate-only scoring hidden failures bucket-level gates
Non-deterministic fixtures noisy regressions fixed seeds and IDs
Mixed pipeline settings invalid comparisons strict run metadata checks

7.2 Debugging Strategies

  • Inspect failing bucket cases with full prompt/evidence traces.
  • Compare retrieval hit rate to faithfulness to isolate stage failures.

7.3 Performance Traps

Running too many high-cost model calls without fixture prioritization.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add simple HTML dashboard for bucket trends.
  • Add fixture quality validator.

8.2 Intermediate Extensions

  • Add noise-level sensitivity analysis.
  • Add per-query-class gating rules.

8.3 Advanced Extensions

  • Auto-generate candidate mitigations from failure patterns.
  • Integrate gate status into deployment pipeline.

9. Real-World Connections

9.1 Industry Applications

  • Reliability gates for enterprise AI releases.
  • Long-context QA assurance pipelines.
  • Evaluation harness ecosystems for LLM systems.

9.3 Interview Relevance

Shows advanced reliability engineering and release governance for LLM memory.

10. Resources

10.1 Essential Reading

  • Lost in the Middle paper.
  • RAG evaluation methodology references.

10.2 Video Resources

  • Talks on long-context reliability and evaluation.

10.3 Tools & Documentation

  • Experiment tracking and report visualization tools.

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain lost-in-the-middle behavior.
  • I can define bucket-level release gates.

11.2 Implementation

  • Harness outputs deterministic bucket metrics.
  • Failures are traceable to stage and fixture.

11.3 Growth

  • I can propose mitigations based on harness evidence.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • deterministic fixture execution + bucket metrics

Full Completion:

  • stage-wise failure attribution + release gate engine

Excellence (Going Above & Beyond):

  • CI/CD integration with automatic rollback recommendations