TurboQuant and the New Economics of Long-Context Inference
Google's TurboQuant compresses KV caches to 3 bits with zero accuracy loss, enabling 6x memory savings and 4x faster lon
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API Model Pricing: Input, Output, Cache and Batch Costs
Why cheapest per-token is rarely cheapest in practice a practical guide to input-output pricing, prompt caching, batch
LLM caching strategies: when, where and how to cache for cost savings
How provider prompt caching, application-level answer caching, semantic caching, and CDN caching layer together for LLM
Context windows explained: why bigger is not always better
A plain-English guide to context windows, long-context trade-offs, and when retrieval or chunking beats stuffing everyth
Open weights vs hosted APIs — practical trade-offs
Should you use managed hosted APIs or deploy open-weight models? The choice involves trade-offs in control, privacy, cos
Function calling and tool use — where agents actually fail
The gap between a polished agent demo and production reliability is measured in tool-use failures. Covers primary failur
What is a token and why does it affect your AI budget?
Tokens are the fundamental currency of LLM computation. Understanding how they work — and why they fluctuate — is critic
Fine-tuning vs prompting vs RAG: the decision checklist
A practical decision checklist to navigate prompting, RAG, and fine-tuning for LLM adaptation, covering cost, latency, f
LLM Ethics in Practice
An analysis of ethical pressure points in the development and deployment of Large Language Models.
LLM Ethics in Practice: A Guide for Builders
A comprehensive examination of operational ethics when deploying LLMs, covering data privacy, hallucination mitigation,
Custom Fine-Tuning ROI — when it pays off vs prompting or RAG
Break-even analysis for custom (self-hosted) vs provider API fine-tuning against prompting and RAG alternatives, coverin
Caching AI answers: when it is safe, risky or pointless
When to cache AI-generated answers to cut costs and latency, when caching risks serving stale or private responses, and
LLM benchmark cheat sheet: what each major benchmark actually tests and which ones matter for real-world performance
Quick-reference to 18 major LLM benchmarks: what each measures, what it misses, real-world correlation ratings, and how
LLM inference cost per query: real-world estimator with worked examples
Estimating cents-per-query for summarisation, RAG, and batch classification with formulas and provider costs across GPT-
LLM API pricing comparison: GPT-5, Claude, Gemini, DeepSeek and Llama in 2026
A per-token comparison of LLM API providers covering hidden costs, cache economics, and context-window pricing cliffs.
Structured outputs and JSON mode: reliability limits
A practical guide to what JSON mode and structured outputs really guarantee, where schema validation still fails, and wh
Output tokens are expensive: designing shorter AI answers without hurting usefulness
Why output tokens cost 3-4x more than input tokens and how to get shorter answers without hurting usefulness.
Model parameters and sizes: why 7B, 70B and MoE labels can mislead
A plain-English explanation of model parameter counts, what 7B, 70B and MoE labels actually tell you, and why bigger num
The hidden cost of retries, fallbacks and validation loops
Why real LLM bills exceed estimates — and how malformed JSON, safety refusals and tool failures multiply API calls.
Rate limits explained: requests, tokens, tiers and hidden launch risks
A practical guide to API quotas, request caps and token limits, and how to plan fallbacks before an AI feature goes live
RAG evaluation: checking retrieval before blaming the model
A practical guide to testing retrieval-augmented generation, spotting whether the retriever or the generator failed, and
RAG costs: vector database, embeddings, reranking and generation
A practical breakdown of RAG costs beyond LLM tokens: embeddings, vector storage, reranking, retrieval, generation, and
Prompt caching explained: when repeated context becomes cheaper
Learn how prompt caching reduces latency and costs for LLM APIs by reusing processed prefixes in repeated contexts.
Multimodal models explained: text, images, audio and video in practical products
A plain-English guide to multimodal AI models: how they combine text, images, audio and video, what each modality costs,
Model routing: using cheap models first without breaking quality
How to route LLM requests across cheap and expensive models using classifier gates, fallback criteria and shadow testing
Long-context benchmarks: needle tests, document QA and real recall
Why needle-in-haystack success is not the same as synthesis over documents, and how to evaluate long-context models with
Local quantized LLM vs frontier model: what changed in the same writing task
A reader-facing comparison of a small local Qwen2.5 7B quantized model and a frontier model writing the same practical L
lm-eval-harness explained for non-researchers
A plain-English guide to what lm-eval-harness does, why teams use it, and why a benchmark runner is not the same thing a
LLM observability cost: logs, traces and evaluation storage
How to budget for monitoring AI systems — retention, redaction, sampling and the hidden cost of knowing what your models
Function-calling benchmarks: why tool-use scores do not guarantee agents work
Understand what function-calling benchmarks actually measure, why leaderboard rankings do not predict production reliabi
Embeddings explained for business search and RAG
What embeddings are, how they turn text into searchable numeric fingerprints, and what to check before buying a vector d
Creating a model scorecard for your own workload
Turn model choice into a repeatable, evidence-led decision with a practical scorecard that measures quality, cost, laten
Benchmark leaderboards for busy buyers: Chatbot Arena, LiveBench and what to ignore
Learn which LLM leaderboards matter for procurement, how to read rank gaps without being misled, and when to run your ow
Batch APIs for LLMs: cheaper, slower and often underused
Learn when batch APIs can cut your LLM costs by 50%, which providers offer them, and how to design batch-first pipelines
AI feature unit economics: cost per user, task and successful answer
Learn to calculate AI feature costs per user, per task and per successful outcome — including hidden costs from retries,
A simple LLM cost calculator editors can maintain
Build a lightweight LLM cost calculator with dated prices, clear assumptions and scenario inputs — no hidden formulas, n
Temperature, top-p and deterministic outputs: what the settings actually do
A guide to LLM parameters: what temperature and top-p control, how deterministic vs creative outputs work, and practical
System prompts, developer prompts and user prompts: who controls what?
A plain-English guide to the prompt hierarchy in LLM apps: what system, developer and user roles mean, how instructions
Prompt length, output length and why AI bills surprise teams
How prompt and output token counts drive LLM billing, and why verbose system prompts, retrieval context, and conversatio