All field notes

Constitutional AI aligns models against an explicit written set of principles rather than relying solely on case-by-case human labelling. This explainer covers how the process works, why an explicit constitution is auditable in a way that implicit preferences are not, its limits, and what the idea implies for enterprise AI policy.

Direct preference optimization aligns a model directly from pairs of preferred and rejected responses, without training a separate reward model. This explainer covers how it works, how it compares to reinforcement learning from human feedback, what preference data is needed, and when this matters for application teams.

Instruction tuning is the training stage that turns a text-predicting model into one that follows directions. This explainer covers what happens during it, how it differs from pretraining and preference optimization, why instruction-tuned models behave the way they do, and what that implies for prompting and custom tuning.

LoRA trains a small set of additional parameters instead of updating a whole model, which makes fine-tuning affordable and adapters swappable at serving time. This explainer covers how it works, what fine-tuning can and cannot teach a model, how multiple adapters are served efficiently, and when retrieval is the better answer.

Parameter-efficient fine-tuning covers the methods that adapt a model by training a small number of additional parameters rather than all of them. This explainer covers the main approaches, how they differ in cost and capability, why they largely match full fine-tuning on adaptation tasks, and how to choose.

A practical framework for choosing an AI development company—production track record, security, communication, and the due diligence that filters out the hype.

Batch inference processes many inputs together on a delayed schedule rather than one at a time on demand. This explainer covers why it costs substantially less than real-time inference, which workloads belong in batch, how to handle partial failure across large jobs, and when the latency trade is not acceptable.

Context engineering is the discipline of deciding what information reaches a model on each call, in what order, and within what token budget. This explainer covers why it matters more than prompt wording, how position affects attention, what to include and exclude, and how to measure whether the context is working.

Inference optimization covers the techniques that make model serving faster and cheaper: caching, batching, quantization, distillation, routing, and speculative decoding. This explainer covers what each does, which preserve output exactly and which approximate it, and how to sequence them for the largest gain with least risk.

Model serving is the infrastructure layer that exposes a model as a reliable production service. This explainer covers what a serving stack actually handles, when self-hosting makes sense against hosted APIs, how cold starts and autoscaling behave with large models, and what a serving layer must monitor.

Speculative decoding speeds up generation by having a small model propose tokens that a larger model verifies in parallel. This explainer covers how it preserves output quality exactly, when the speedup is worth having, what it costs in memory and operational complexity, and where it does not help.

Tokenization is how a model converts text into the units it actually processes. This explainer covers why a word is not a token, why the same content costs more in some languages than others, how tokenization affects context limits and billing, and the subtle bugs it causes in production systems.

Top-p sampling, also called nucleus sampling, limits token selection to the smallest set whose probabilities sum to a threshold. This explainer covers how it works, how it interacts with temperature and top-k, why default settings are wrong for many production tasks, and how to choose values deliberately.

AI project costs vary wildly with scope, data, and integration. How to estimate realistically—and why a discovery beats a blind quote every time.

Competitor monitoring produces daily digests nobody reads and misses the changes that matter. This guide covers restricting collection to legitimate public sources, distinguishing material change from marketing noise, routing findings to the people who can act, and drawing clear ethical and legal boundaries around collection.

Machine translation is cheap and product localization still fails, because translation without context, terminology control, and native review produces fluent text that is wrong. This guide covers glossary enforcement, passing the context a translator needs, tiering locales by quality requirement, and drawing a firm line around regulated content.

Research agents produce confident summaries built on whatever they found first, which is how a blog post's guess becomes a strategy assumption. This guide covers tiering sources by quality, citing every claim, surfacing contradictions instead of averaging them, and keeping synthesis and recommendation with analysts.

Public reviews are the most honest feedback a product gets and the least systematically used. This guide covers collecting across sources, handling inauthentic reviews without pretending they do not exist, extracting specific actionable issues, correlating complaints with releases, and building a response workflow that does not sound automated.

Open-text survey responses hold the insight and get skimmed because coding them by hand does not scale. This guide covers automated coding with stable themes, measuring agreement against human coders, being honest about who actually responded, keeping every theme traceable to real quotes, and leaving interpretation to researchers.

An embedding model converts text, images, or other data into numeric vectors whose distances reflect semantic similarity. This explainer covers how embeddings work, why model choice matters more than most teams expect, where they are used in retrieval and classification, and how to evaluate them on your own data.

Data quality problems are discovered by the executive reading the dashboard, which is the most expensive possible detection point. This guide covers inferring expectations from history, checking freshness and volume before content, routing alerts by lineage to the owner who can fix the source, and publishing trust signals consumers can act on.

Documentation fails because it goes stale silently, not because nobody wrote it. This guide covers grounding drafts in code and configuration rather than imagination, detecting staleness against the artefacts documentation describes, triggering updates on change rather than on calendar, and keeping the reasoning that matters under human authorship.

Log platforms hold the answer to most production questions and make engineers learn a query language to find it. This guide covers generating correct queries from natural questions, framing anomalies against real baselines, correlating with deploys and changes, controlling query cost, and keeping the engineer's judgement in the loop.

User-reported phishing is a high-volume queue that security teams answer slowly, which teaches users to stop reporting. This guide covers analysing reported messages safely, producing verdicts with calibrated confidence, replying to reporters quickly, detecting campaigns across the estate, and keeping mailbox remediation under human authority.