Google's New SDLC for Vibe Coding — The Missing Guide
Google engineers formalized a six-phase framework for AI-assisted development that maps the spectrum from casual prompt-
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Google Releases Gemini 3.5 Live Translate — Real-Time Voice Preservation Translation
Google DeepMind's Gemini 3.5 Live Translate translates spoken audio between 70+ languages in real time while preserving
Fine-Tuning LLMs: A Practical Step-by-Step Guide
A practical walkthrough of the full fine-tuning lifecycle — from choosing your method to deploying a working model you c
Multimodal LLMs for production: when to use image, audio and video capabilities
A practical decision framework for adding visual, audio and video inputs to LLM products — covering costs, latency, accu
Prompt Injection Explained for Business Users
A practical security guide to prompt injection — how attackers hijack AI models, what business users need to know, and t
Building a custom LLM chatbot: end-to-end guide from specification to deployment
A staged guide for teams building their own LLM chatbot: define scope, choose architecture, implement, test, and deploy
LLM monitoring and alerting: tracking performance, cost, and reliability in production
Build practical alerting for LLM apps — four failure modes, tiered thresholds, rolling baselines, and how to avoid alert
Multi-model routing and gateway architecture — routing queries to the right model based on cost, latency, and capability
A practical framework for multi-model routing: how to design gateways, routing policies, and fallback chains that optimi
LLMs for software engineering and DevOps: a practical guide
How LLMs fit into code review, test generation, incident response, and DevOps workflows — what works, what doesn't, and
LLM security hardening for production — input validation, output filtering, rate limiting and PII scanning
A practical guide to the four security layers every production LLM application needs: input validation, output filtering
LLM cost optimisation playbook: practical techniques for reducing API spend
A structured framework for diagnosing and reducing LLM API costs: prompt optimisation, caching, output control, model ro
Fine-tuning LLMs: a practical step-by-step guide
From data prep to deployment: how to fine-tune an LLM with a concrete worked example, covering LoRA setup, training conf
LLM observability stack: logging, tracing, monitoring, and cost tracking
Compare five LLM observability tools—LangSmith, Arize, Helicone, Weights & Biases, and Datadog—with setup guidance and a
MCP implementer's guide: setting up Model Context Protocol servers, tools, and security patterns
A step-by-step tutorial for building MCP servers in Python and Node, exposing tools and resources, connecting to desktop
LLMs for startups vs enterprises: when to build, when to buy
Decision framework for API-based vs self-hosted LLM: when team size, budget, latency and data sensitivity push you towar
Tool use and function calling: a practical implementation guide
A step-by-step guide to implementing LLM function calling in production: defining schemas, handling parallel calls, erro
Golden datasets for LLM products: how small regression sets prevent regressions
A practical explanation of why a small, stable test set is often more useful than a huge benchmark when you need confide
The model release treadmill: how to avoid rebuilding every month
A practical guide to decoupling your product from provider churn so every model update does not become a rewrite.
Vector databases: when semantic search is enough and when it is not
A practical guide to deciding whether you need a vector database, a search index or something much simpler.
Schema-first AI extraction: making LLMs useful for messy documents
How to extract structured data from unstructured documents using LLMs: schema design, confidence flags, validation, and
Prompt versioning: treating prompts like production code
How to manage prompt changes in teams: version control, eval-linked releases, approval workflows, rollback strategies, a
MCP explained: tools, resources, prompts and the current hype gap
A clear guide to what Model Context Protocol is, what it is not, and why marketing sometimes runs ahead of the wiring.
Tool-use safety: stopping agents from taking dangerous actions
A practical guide to approval gates, least privilege, dry runs and audit logs for AI agents with tools.
The evidence-led AI website manifesto: how theLLMs will review claims
How theLLMs reviews claims, sources content, dates evidence, and handles uncertainty. A public editorial standard for tr
Safe prompt templates: reducing brittle instructions and hidden assumptions
Why prompts fail silently, how to treat prompts as tested product assets, and the versioning, testing and acceptance cri
Refusals and over-refusals: testing whether safety blocks useful work
A plain-English guide to distinguishing sensible safety boundaries from over-refusal that breaks legitimate use cases.
Red teaming an LLM feature: a practical first-week checklist
A practical first-week checklist for finding failure modes in a new LLM feature — with concrete test items, sample promp
PII handling for LLM apps: minimisation before redaction
A practical guide to reducing personal-data exposure in AI features by minimising what you send before you try to redact
Model drift without training: why API behavior changes over time
Why hosted LLMs change their outputs even without a version bump, and how pinned models, eval regression sets, and chang
Rerankers explained: the quiet quality layer in RAG systems
How rerankers improve retrieval precision with a worked example, model names, latency numbers, and a decision framework
LLM observability basics: traces, prompts, evals and feedback loops
A framework for monitoring LLM applications in production: what to trace, which metrics matter, how to sample prompts, a
- "Access- control- for- RAG:- why- retrieval- permissions- matter- before- generation
- "Retrieval- permissions- must- be- enforced- before- generation,- not- after.- Learn- where- teams- get- document-leve
- "Human-in-the-loop- AI:- approval- queues- that- do- not- become- bottlenecks
- "How- to- design- human- review- for- AI- outputs- that- catches- real- failures- without- slowing- down- every- routi
- "Jailbreaks- vs- product- safety:- what- operators- can- realistically- control
- "A- practical- guide- to- separating- model-level- safety- from- app-level- permissions,- tool- boundaries- and- opera
- "Latency- in- LLM- apps:- first- token,- total- time- and- user- experience
- "A- plain-English- guide- to- why- AI- features- feel- slow,- what- to- measure,- and- how- to- separate- queueing,- m
- "Hallucination- testing:- how- to- build- a- small- regression- set
- "A- practical- guide- to- creating- a- small- test- set- for- unsupported- claims,- regression- checking- and- safer-
- "Inference- vs- training- vs- fine-tuning:- three- terms- operators- confuse
- "A- plain-English- guide- to- the- three- phases- of- model- work,- what- each- one- changes,- and- what- the- differe
Editorial rule
Every guide needs a stopping point
- Define the job, input, output, assumptions, and failure modes.
- Show the checks that prove the workflow worked, or say what remains unproved.
- Link back to Cache for concepts instead of re-explaining everything on every page.
- Keep tool/provider claims dated. LLM tooling ages like milk in a warm server rack.