The Hallucination Problem in Production Hallucination — LLMs generating confident, fluent text that is factually wrong — is the most discussed quality problem in LLM deployment and, in practice, one of the most manageable once you understand its causes and patterns. The framing of hallucination as a random, unpredictable failure mode is misleading. Hallucination follows … Read more
Why LLM Observability Is Different from Traditional Monitoring Monitoring a traditional API service means tracking uptime, latency, error rates, and throughput. These metrics matter for LLM services too, but they capture only a fraction of what can go wrong. An LLM endpoint can return HTTP 200 with low latency and still produce responses that are … Read more
The Customer Support Opportunity Customer support is one of the highest-volume, most measurable, and most immediately impactful applications of LLMs in enterprise. The use case is clear: a significant fraction of support tickets are repetitive, well-defined queries that follow predictable patterns and can be resolved with information that exists in documentation. Automating these frees human … Read more
What Embeddings Are and Why They Matter An embedding is a numerical representation of a piece of text — a vector of floating-point numbers that encodes the semantic meaning of the text in a form that mathematics can operate on. Two sentences with similar meanings will have embeddings that are close together in the high-dimensional … Read more
Why Rate Limits Bite in Production LLM API rate limits are one of those problems that developers do not take seriously until the first time they hit them in production — and then they take them very seriously. Unlike a database or internal service that you can scale yourself, LLM API rate limits are externally … Read more
Why Alignment Matters for Builders AI alignment is the challenge of ensuring that AI systems do what their designers and users intend, and nothing they do not. For LLM application builders, alignment is not an abstract research concern — it is a practical engineering reality that affects every system you deploy. The models you are … Read more
Beyond the Basics: Why Advanced Cost Optimisation Matters Most teams deploying LLMs learn the obvious cost levers quickly: use cheaper models for simple tasks, enable prompt caching, keep prompts concise. These basics can reduce costs by 30–50%. But the teams achieving 70–90% cost reductions without quality loss are applying a more systematic, layered approach that … Read more
What Are Knowledge Graphs and Why Pair Them with LLMs? A knowledge graph is a structured representation of entities and the relationships between them, stored as a network of nodes (entities) and edges (relationships). Where a relational database stores facts in tables with rows and columns, a knowledge graph stores facts as triples: subject, predicate, … Read more
Three Approaches, One Question When an LLM does not behave the way you need it to, three distinct strategies are available to improve its performance: prompting (changing what you say to the model at inference time), retrieval-augmented generation (providing relevant external information at inference time), and fine-tuning (changing the model’s weights through training). These are … Read more
The Data Analysis Transformation Data analysis has historically required either technical skill (SQL, Python, R) or expensive analyst time to translate business questions into code and results. LLMs are disrupting both requirements. They can write SQL and Python from natural language descriptions, explain complex data in plain language, generate visualisation code from data summaries, identify … Read more
