Guide posts

Evaluating Voice Agents: Beyond Word Error Rate

Did you know that a voice agent boasting a near-perfect 98% Speech-to-Text accuracy rate can still drive customers to hang up in frustration within...

Building Your First MCP Server: A Step-by-Step Tutorial

44 min read

Building Your First MCP Server: A Step-by-Step Tutorial

What if the biggest bottleneck in AI today isn't the intelligence of the model itself, but how poorly it connects to the local data, secure databases, and...

EU AI Act Compliance in 2026: What You Must Do

EU AI Act Compliance in 2026: What You Must Do

Practical 2026 EU AI Act compliance guide — risk tiers, GPAI obligations, deadlines (Aug 2026), penalties, and the steps builders need to take this quarter.

Evaluating AI Vendors: A Procurement Checklist

Evaluating AI Vendors: A Procurement Checklist

A 2026 procurement-grade AI vendor checklist — data handling, security, evals, output liability, escape hatches, and the red flags to watch for.

Hallucination Detection: Techniques That Actually Work

Hallucination Detection: Techniques That Actually Work

A 2026 guide to LLM hallucination detection — grounding verification, self-consistency, classifier-based detection, and how to stack techniques in production.

Model Quantization in 2026: 4-bit, 8-bit, and the Tradeoffs

Model Quantization in 2026: 4-bit, 8-bit, and the Tradeoffs

A 2026 guide to model quantization — GPTQ, AWQ, GGUF, FP8, and INT8 — with quality-vs-speed tradeoffs, hardware support, and a practical serving recipe.

Tutorial: Build a Production RAG App in 2 Hours

Tutorial: Build a Production RAG App in 2 Hours

A practical 2026 RAG tutorial — chunking, hybrid retrieval, reranking, citations, and eval. Production-grade Python code for OpenAI, Qdrant, Cohere.

Tutorial: Fine-Tune Llama 4 Scout for Your Domain

Tutorial: Fine-Tune Llama 4 Scout for Your Domain

A 2026 hands-on tutorial for fine-tuning Llama 4 Scout — LoRA setup, dataset prep, training, eval, deployment. Concrete Python code with Unsloth.

Tutorial: Stream LLM Responses from a FastAPI Backend

Tutorial: Stream LLM Responses from a FastAPI Backend

A 2026 production-grade FastAPI streaming tutorial — SSE, async, post-stream usage tracking, client-disconnect handling, and observability.

Pin Your Models: A Survival Guide for Unstable AI Defaults in Production

4 min read

Pin Your Models: A Survival Guide for Unstable AI Defaults in Production

Why "default" model aliases are dangerous in production, how to pin AI model versions safely, and what to do when a vendor deprecates yours.

Prompt Caching Explained: Anthropic, OpenAI, and the Math

5 min read

Prompt Caching Explained: Anthropic, OpenAI, and the Math

How prompt caching works at Anthropic and OpenAI in 2026 — cache breakpoints, write and read pricing, TTL, breakeven math, and how to design cache-friendly prompts.

Rate Limiting AI APIs: Strategies That Actually Work

Rate Limiting AI APIs: Strategies That Actually Work

A 2026 guide to AI API rate limiting — token bucket, sliding window, per-tenant fairness, 429 handling, and Redis-backed scale patterns.

LoRA and Distillation: A Practical Guide for 2026

LoRA and Distillation: A Practical Guide for 2026

A 2026 practical guide to LoRA, QLoRA, and distillation — when to use each, default hyperparameters, dataset quality, the toolchain, and shipping to production.

Load Balancing AI Workloads: Routing Across Providers

Load Balancing AI Workloads: Routing Across Providers

A 2026 guide to load balancing AI workloads — gateway patterns, multi-provider failover, latency-aware routing, caching, cost guardrails, and observability.

Mitigating AI Bias in Production Systems

Mitigating AI Bias in Production Systems

A practical 2026 guide to mitigating AI bias in production — slice evals, counterfactual testing, mitigation techniques that work, and the limits of the field.

AI Inference Cost Optimization: Practical Wins

AI Inference Cost Optimization: Practical Wins

Concrete tactics to cut LLM inference cost in 2026 — prompt caching, model cascading, batching, smaller models, and observability. With the math and a worked example.

RAG Best Practices in 2026: Chunking, Reranking, Hybrid Search

RAG Best Practices in 2026: Chunking, Reranking, Hybrid Search

The 2026 RAG playbook — chunking strategies, hybrid retrieval, rerankers, and how long context fits in. Practical defaults and the four levers that move quality.

Streaming AI Responses: SSE, WebSockets, and the Pitfalls