13 posts tagged "LLM"
When AI makes code cheap to generate, understanding and verification become the bottleneck. How headcount, roles, review, hiring, and health metrics change for AI-native teams.
How to judge AI agent startup defensibility across four axes: workflow data, integration depth, evaluation infrastructure, and operating track record, plus the DD metrics to check.
Prompting, RAG, and fine-tuning solve different problems. A decision framework based on data volume, update frequency, desired behavior, and cost, plus a staged rollout.
Why LLMs produce fluent but false output, where hallucinations hurt most in business use, and how grounding, RAG, verification steps, and scoped use cases keep the risk manageable.
Traditional unit tests cannot guarantee LLM product quality. This piece covers the three types of evals, a maturity model, and the questions to check in technical due diligence.
LLM inference cost is driven by token volume, model choice, and call count. Mismatched with flat-rate pricing, it can make margin shrink as usage grows — here's how to spot and fix it.
An AI agent is an LLM that autonomously calls external tools to finish multi-step tasks. This guide distinguishes chat, workflows, and agents, and covers function calling, MCP, and deployment risks.
RAG lets an LLM search your documents and answer using what it finds. We explain how it differs from fine-tuning and why in-house chatbots built on it often disappoint.
Internal chatbots usually fail not because the model is weak, but because the source documents and retrieval setup were never properly prepared. Here is how to diagnose and fix it.
Anthropic's 2026 study found a 3x gap between what AI could do (94% of computing tasks) and what it actually does (33%). What observed exposure means for investors and technical DD.
Four LLM integration patterns (API, RAG, Fine-tuning, custom) compared across cost, scale, and risk. A decision framework built on accuracy, data confidentiality, cost, and speed axes.
A five-axis framework for evaluating AI startup moats: data, domain integration, UX, domain knowledge, and distribution. Separates durable advantages from temporary differentiation.
Technical DD for AI-powered startups requires four dimensions beyond the traditional framework: LLM usage classification, prompt IP, eval systems and cost structure, and safety guardrail design.
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