Blog
- 121Apr 11, 20268 minreliabilitysre
SLOs for Non-Deterministic Systems: Defining Reliability When Every Response Is Different
Traditional SLIs like latency and error rate miss the dominant failure mode of AI systems — correct execution, wrong answer. A practical framework for semantic SLOs, error budgets at 85% baselines, and alerting architectures that distinguish real degradation from normal variance.
Read more → - 122Apr 11, 202610 mininference-optimizationspeculative-decoding
Speculative Decoding in Practice: The Free Lunch That Isn't Quite Free
How speculative decoding cuts LLM inference latency 2-3x by drafting tokens with a small model and verifying in parallel — plus the draft model selection math, batch size tradeoffs, and production pitfalls that determine whether you get a speedup or a slowdown.
Read more → - 123Apr 11, 202612 minai-architecturellm
Stateful vs. Stateless AI Features: The Architectural Decision That Shapes Everything Downstream
The choice between stateful and stateless AI features is made early and felt everywhere — in your storage layer, your debugging toolchain, your security posture, and your costs. Here's how to make it deliberately.
Read more → - 124Apr 11, 20269 minstructured-outputsconstrained-decoding
Structured Outputs and Constrained Decoding: Eliminating Parsing Failures in Production LLMs
Constrained decoding guarantees schema-valid LLM output at the token level, removing retry logic and parsing heuristics from production pipelines — but research shows a 17% creativity cost that demands a clear decision framework.
Read more → - 125Apr 11, 20268 minsynthetic-datamodel-collapse
Synthetic Data Pipelines That Don't Collapse: Generating Training Data at Scale
Model collapse silently degrades LLMs trained on their own output. Learn the pipeline architecture — accumulative mixing, multi-source generation, verification stacks, and diversity monitoring — that keeps synthetic training data productive instead of poisonous.
Read more → - 126Apr 11, 202610 minai-product-strategydefensibility
The AI Wrapper Trap: When Your Moat Is Someone Else's API Call
Why thin-wrapper AI startups face existential risk every model release cycle — and the three defensibility layers (proprietary data flywheels, domain-specific evals, workflow integration) that separate survivors from cautionary tales.
Read more → - 127Apr 11, 202611 minai-autonomyhuman-in-the-loop
The Autonomy Dial: Five Levels for Shipping AI Features Without Betting the Company
A five-level framework for graduating AI features from suggestion to full autonomy, with concrete metrics at each transition, leading indicators for dialing back, and the bounded autonomy pattern that maps decision risk to oversight level.
Read more → - 128Apr 11, 202610 minllm-calibrationproduction-ai
The Calibration Gap: Your LLM Says 90% Confident but Is Right 60% of the Time
LLM confidence scores routinely overstate accuracy by 30–80 percentage points. How to measure the calibration gap with reliability diagrams and ECE, fix it with temperature scaling and adaptive recalibration, and design production systems that stay reliable when confidence lies.
Read more → - 129Apr 11, 20269 minagent-memoryai-engineering
The Forgetting Problem: When Unbounded Agent Memory Degrades Performance
Unbounded agent memory stores silently degrade performance as stale facts, cross-context contamination, and error propagation accumulate. Practical forgetting strategies — time-based decay, access-frequency reinforcement, selective addition, and active consolidation — plus the eval methodology to measure whether memory is helping or hurting.
Read more → - 130Apr 11, 20267 minllmprompt-engineering
The Instruction-Following Cliff: Why Adding One More Rule to Your System Prompt Breaks Three Others
LLM compliance doesn't degrade linearly — it hits a cliff where adding one more rule destabilizes others. Research shows even frontier models cap at 68% accuracy under high instruction density. Here's why rules fight each other and how decomposition patterns keep your system prompt reliable.
Read more → - 131Apr 11, 20268 minobservabilityllm-ops
The Observability Tax: When Monitoring Your AI Costs More Than Running It
AI workloads generate 10–50x more telemetry than traditional services, pushing monitoring bills past inference costs. A practical guide to tiered sampling, retention policies, and tool consolidation that cuts observability spend by 50–90% without losing signal.
Read more → - 132Apr 11, 202610 minai-agentstoken-optimization
The Planning Tax: Why Your Agent Spends More Tokens Thinking Than Doing
LLM agents burn 40-70% of their token budget on planning before executing a single tool call. A breakdown of where reasoning tokens go, why more thinking doesn't always mean better outcomes, and the architectural patterns — ReWOO, plan caching, hierarchical decomposition — that reclaim your budget.
Read more →
About Tian Pan
I'm Tian Pan, an engineer-founder focused on agentic engineering — building autonomous AI systems and scaling engineering teams. I write practical guides on system design, technical leadership, and shipping with AI agents. Previously an early engineer at Uber, Brex, and IoTeX.
