Technical blog post: "Implementing Contextual Bandits for LLM Routing"

[evanvolgas]

Overview

Write technical blog post showcasing conduit-benchmark results and contextual bandit implementation.

Background

From STAFF_REVIEW.md Initiative 3: "Technical Writing - 2 blog posts within 6 months"

Goal: Attract users, demonstrate expertise, share research findings

Proposed Structure

Title

"Implementing Contextual Bandits for LLM Routing: 40% Cost Savings with LinUCB"

Sections

1. The Problem (300 words)

• LLM routing wastes money on over-powered models
• Static routing (always GPT-4) is expensive
• Manual routing rules don't generalize
• Need: Dynamic routing that learns optimal model selection

2. The Solution: Contextual Bandits (400 words)

• What are multi-armed bandits?
• Why contextual (LinUCB) beats non-contextual (UCB1)
• Query features matter: complexity, category, length
• Exploration vs exploitation tradeoff

3. Implementation Details (600 words)

• LinUCB with Woodbury identity for O(d²) updates
• PCA dimensionality reduction (67 dimensions)
• Hybrid UCB1→LinUCB strategy for cold start
• Code examples from conduit-benchmark

# Example: LinUCB routing decision
router = LinUCBBandit(models=MODEL_REGISTRY)
context = extract_features(query)  # 67-dim vector
model = router.select_arm(context)  # LinUCB decision
response = await model.execute(query)
quality = await evaluate(response)
router.update(model, quality, context)  # Learn

4. Benchmark Results (500 words)

• Main experiment: N=2,500, 11 algorithms, 3 runs
• Winner: LinUCB or Thompson Sampling (from Issue Statistical analysis and publication-ready report generation #23 results)
• Cost savings: ~40% vs always-GPT-4
• Quality maintenance: 95%+ vs Oracle
• Convergence: LinUCB converges in ~2,000 queries
• Visualizations: Regret curves, Pareto frontier

5. Production Lessons (300 words)

• Cold start problem and hybrid solutions
• Hyperparameter tuning (exploration coefficient)
• When to use contextual vs non-contextual
• Real-world deployment considerations

6. Try It Yourself (200 words)

pip install conduit-benchmark
conduit-bench generate --queries 1000
conduit-bench run --algorithms linucb,thompson,ucb1
conduit-bench analyze --results results/

Publishing Platforms

1. Dev.to - Developer community, good SEO
2. Medium - Broader audience, behind paywall
3. Personal blog - Full control, SEO benefits
4. Company blog (if applicable)

Recommendation: Publish on Dev.to + cross-post to Medium

SEO Keywords

• contextual bandits
• llm routing
• multi-armed bandits
• cost optimization
• linucb
• thompson sampling
• ai cost reduction

Success Criteria

• Blog post drafted (2,300 words)
• Benchmark results included (from Issue Statistical analysis and publication-ready report generation #23)
• Code examples tested and working
• Visualizations embedded (regret curves, etc.)
• Published on Dev.to (or chosen platform)
• Cross-posted to Medium (optional)
• Shared on relevant communities (Reddit r/MachineLearning, HN)
• Linked from conduit-benchmark README

Timing

Target: Publish within 2 weeks of main experiment completion (Issue #23)

Dependencies

• Issue Statistical analysis and publication-ready report generation #23: Statistical analysis complete (need results for Section 4)
• Issue PyPI publication preparation #28: PyPI publication (for "Try It Yourself" section)

Priority

LOW - Important for external adoption, but not blocking research

Difficulty

Beginner - Writing and communication, not technical implementation

Resources

• Technical Writing for Engineers
• Dev.to Publishing Guide
• Example: Solving the Multi-Armed Bandit Problem