Blog, page 2

Build an LLM Evaluation Harness That Can Block a Release

From task contracts and test slices to calibrated judges, regression budgets, and production feedback loops.

Hallucination Controls Belong in the System, Not One Prompt

A layered design for constraining claims, grounding evidence, verifying outputs, and abstaining when the system does not know.

Guardrails as Policy Enforcement, Not Keyword Blocking

Building layered input, action, and output controls that remain testable when language and threats change.

Threat Modeling an AI Application End to End

Assets, trust boundaries, model-specific attacks, tool abuse, data leakage, and concrete mitigations for deployed AI systems.

Prompt Injection Defense in Depth

Why delimiters are not a sandbox, how indirect injection reaches agents, and which architectural controls actually reduce impact.

Embedding Systems: Models, Indexes, and Semantic Drift

Selecting representations, constructing embedding inputs, migrating indexes, and detecting when semantic neighborhoods stop serving the product.

Fine-Tuning with LoRA: Data, Adapters, and Production Operations

When fine-tuning is justified, how low-rank adapters work, and what it takes to evaluate, serve, and update them safely.

Multimodal AI Pipelines for Images, Audio, and Documents

Engineering preprocessing, temporal and spatial grounding, context budgets, validation, and storage around multimodal models.

LLM Cost Engineering: From Token Prices to Unit Economics

Modeling full request cost, eliminating waste, forecasting margins, and optimizing without disguising quality regressions.

Observability for LLM and Agent Systems

Tracing model calls, retrieval, tools, state transitions, quality signals, and cost without turning telemetry into a privacy liability.

Model Routing, Cascades, and Fallbacks

How to choose models per request using task risk, calibrated confidence, operational health, and total expected cost.

Structured Outputs Beyond Valid JSON

Designing schemas, constrained decoding, semantic validators, repairs, and safe evolution for dependable model integrations.