AI
The Cost Model of LLM Features: Estimating and Controlling Spend
A framework for estimating LLM feature costs before shipping and the concrete levers that control spend once traffic scales beyond a prototype.
3 min read
Category
AI
20 articles
AI
Streaming LLM Responses: UX and Backend Plumbing
How to implement LLM response streaming end to end, from server-sent events to handling structured output and mid-stream tool calls.
3 min read
AI
Multimodal Models in Real Products: Use Cases and Constraints
Where multimodal LLMs earn their cost in production products today, and the practical constraints around images, documents, and audio.
3 min read
AI
Tool Calling Design Patterns for LLM Applications
Practical patterns for designing tool schemas and interfaces that models call reliably, from naming conventions to error surface design.
3 min read
AI
Observability for LLM Applications: Tracing, Tokens, and Feedback Loops
What to actually instrument in an LLM application beyond latency and error rate, and how to close the loop from production traffic to eval sets.
3 min read
AI
Quantization Explained: What You Lose and What You Gain
A practical breakdown of LLM quantization techniques, the real quality trade-offs behind them, and how to decide how far to push it.
3 min read
AI
LLM Inference Optimization: Batching, KV Cache, and Speculative Decoding
The core techniques that make LLM inference fast and affordable at scale: batching, KV cache management, and speculative decoding explained.
3 min read
AI
Hybrid Search: Combining BM25 and Vector Similarity
Why pure vector search underperforms on keyword-heavy queries, and how to combine it with BM25 using score fusion that actually works.
3 min read
AI
Semantic Caching for LLM Cost Reduction
How semantic caching cuts LLM spend by reusing responses to meaningfully similar queries, and the correctness traps that come with it.
3 min read
AI
Guardrails for LLM Apps: Input/Output Filtering and Injection Defense
Layered defenses against prompt injection, unsafe outputs, and data leakage in production LLM applications, and where each layer actually helps.
3 min read
AI
Structured Outputs from LLMs: JSON Schemas and Validation
How to reliably get valid, typed JSON out of an LLM in production, from schema-constrained decoding to validation and repair strategies.
3 min read
AI
How Tokenization Works and Why Engineers Should Care
A practical look at subword tokenization internals and the concrete ways tokenizer behavior affects cost, latency, and model quality.
3 min read
AI
Managing Context Windows: Chunking, Summarization, and Memory
Practical strategies for fitting long conversations and large documents into a context window without losing what actually matters.
3 min read
AI
Evaluating LLM Applications: Offline Evals and Regression Suites
How to build an eval suite that catches LLM application regressions before deploy, from labeled datasets to LLM-as-judge pitfalls.
3 min read
AI
Vector Database Selection Criteria for 2026
The technical criteria that actually predict whether a vector database will hold up in production, beyond raw ANN benchmark numbers.
3 min read
AI
Fine-Tuning vs RAG vs Prompting: A Decision Framework
A practical framework for deciding whether your LLM problem needs prompting, retrieval, fine-tuning, or some combination of the three.
3 min read
AI
Building Reliable LLM Agents: Planning, Tools, and Recovery
Why most agent failures are engineering problems, not model problems, and the planning, tool design, and recovery patterns that fix them.
3 min read
AI
Prompt Engineering Patterns That Survive Model Upgrades
Prompting techniques and habits that keep working across model version bumps, plus the brittle patterns that quietly break every upgrade.
3 min read
AI
Choosing and Evaluating Embedding Models for Retrieval
How to pick an embedding model for your retrieval stack using benchmarks that actually correlate with your data, not just MTEB leaderboard rank.
3 min read
AI
RAG Architecture in Production: What Actually Breaks
A field guide to the parts of retrieval-augmented generation that fail under real traffic, real documents, and real users.
3 min read