The Economics of AI
LLM Systems
Every token costs. Every query consumes. As AI systems scale, their viability is determined not just by capability, but by the economics of profitable usage.
Dec 11
Blog
Short observations on technology, systems, and how things work.
12 articles
Nov 19
LLMs for Code Generation
Development
LLMs accelerate coding workflows from prototyping to debugging. They augment, not replace — leverage increases while responsibility remains.
Oct 23
Observability in LLM Systems
Infrastructure
As systems grow in complexity, visibility becomes a prerequisite for control. Track prompts, outputs, latency, and cost — without observability, failure is silent.
Sep 16
The Role of Embeddings
LLM Systems
Embeddings convert meaning into vectors, powering search, clustering, and recommendation. They are the semantic layer — without embeddings, there is no context.
Aug 27
Local vs Cloud LLMs
Architecture
Cloud offers power and scale. Local offers privacy and cost control. The future is hybrid: cloud for complexity, local for routine.
Jul 14
Design Patterns for LLM Applications
Development
As the field matures, ad hoc experimentation gives way to structured design. Key patterns include prompt templates, RAG, tool use, agent loops, and guardrails.
Jun 9
Hallucination Mitigation
LLM Systems
LLMs generate plausible text, not truth. Strategies for grounding, validation, and containment — trust is engineered, not assumed.
May 20
Latency vs Intelligence
Performance
In production environments, intelligence is constrained by time. Faster responses often outperform better ones — optimize for intelligence per second.
Apr 8
Vector Databases Explained
Infrastructure
Vector databases store embeddings — numerical representations of meaning — enabling semantic search beyond keywords. Poor retrieval leads to poor generation.
Mar 25
RAG vs Fine-Tuning
LLM Systems
RAG injects knowledge dynamically. Fine-tuning embeds behaviour directly. The emerging pattern is hybrid: RAG for knowledge, fine-tuning for behaviour.
Feb 18
The Rise of Agentic Workflows
Architecture
Single LLM calls are giving way to agentic systems that observe, decide, act, and repeat. The future is not smarter outputs, but controlled workflows with bounded autonomy.
Jan 13
From Prompt to Product
Development
A prompt is not a product — it's a fragile prototype. The transition requires three structural layers: context control, state and memory, and tool integration.