AI Insights

AI strategy playbooks for product, finance, and engineering

Deep dives on AI model fundamentals, transformer benchmarks, model selection frameworks, prompt optimization, and production operations. Use these articles alongside the AI Agent Cost Calculator and Model Library to make confident roadmap calls.

AI Fundamentals

AI Model Foundations and Terminology

Understand the difference between base, instruct, and fine-tuned LLMs, what makes a model multimodal, and how context windows shape product decisions.

  • How transformer architecture enables sequence understanding
  • Key terminology: parameters, context windows, temperature
  • When to choose base, instruct, or domain fine-tuned checkpoints

Model Landscape

Transformer Model Landscape in 2025

Compare OpenAI, Anthropic, Google, Meta, Mistral, and Chinese frontier models by capability, pricing, and best-fit workloads.

  • Capability tiers for reasoning, multimodality, and throughput
  • Pricing snapshots across proprietary and open-source vendors
  • Deployment considerations for regulated industries and global teams

Evaluation Framework

Choosing the Right Model for Your AI Agent

A decision framework for matching tasks with the right model tier, including cascades, specialization, and budget guardrails.

  • Map task complexity and latency requirements to model families
  • Design fallback cascades to balance cost and success rate
  • Checklist for vendor due diligence and compliance reviews

Prompt Engineering

Prompt Optimization and Retrieval Techniques

Improve output quality with structured prompting, retrieval-augmented generation, and continuous evaluation workflows.

  • Prompt templates that encourage reliable tool use and chain-of-thought
  • Retrieval strategies to keep knowledge fresh without ballooning tokens
  • Offline evaluation loops for regression testing prompts and datasets
10 min read Read article

Operations

Deploying and Observing AI Agents at Scale

Operational playbook for rolling out AI agents safely with guardrails, observability stacks, and cost governance.

  • Deploy pipelines across staging, shadow modes, and gradual rollouts
  • Instrument latency, quality, and cost dashboards for all stakeholders
  • Enforce guardrails for security, compliance, and escalation workflows

Need help modeling agent cost end-to-end?

Combine these AI insights with our token estimation guide and calculator scenarios to pressure-test budgets, evaluate fallback cascades, and align stakeholders before launch.