🔨 Building with AI

AI-Assisted Software Development

GitHub Copilot, Cursor, Replit AI — these tools write, explain, and refactor code. Non-technical leaders should understand what they enable: faster prototyping, smaller teams, lower build costs.

• Cursor users report 30–50% reduction in time-to-feature for experienced developers.
• AI code tools are most valuable for boilerplate, tests, and documentation.
• Non-technical founders can now ship MVPs using AI coding tools with basic guidance.

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Low-Code / No-Code AI

Bubble, FlutterFlow, and Webflow AI allow business teams to build functional apps without engineers. The ceiling is real — complex products still need developers — but MVPs are now a business team task.

• Bubble: 5M+ users, used by Y Combinator startups for MVP validation.
• FlutterFlow AI generates React Native mobile apps from design prompts.
• Time-to-MVP with no-code AI: 2–4 weeks vs. 3–6 months with traditional dev.

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Retrieval-Augmented Generation in Practice

RAG is the most practical AI pattern for enterprise: ingest your documents, enable AI to search and cite them. This turns generic AI into a company-specific knowledge assistant.

• RAG reduces hallucination by 60–90% by grounding answers in source documents.
• Vector databases (Pinecone, Weaviate, pgvector) store document embeddings.
• Cost: ingesting 100K pages into RAG costs ~$50–200 in embedding API calls.

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Fine-Tuning: When and Why

Fine-tuning adjusts model weights to make an AI better at specific tasks with specific tone. It's expensive and often unnecessary — RAG and prompt engineering solve most cases cheaper.

• Fine-tuning a 7B model costs $50–500 on cloud GPUs.
• Requires ~1,000+ high-quality examples to meaningfully improve behaviour.
• Use case: a legal AI that must match your firm's specific drafting style.

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API Integration Patterns

The simplest enterprise AI pattern: call an LLM API (Claude, GPT-4) from your existing application, passing context and getting output. This doesn't require ML expertise — it's standard software integration.

• Anthropic Claude API: $15/million output tokens for Sonnet (mid-2024 pricing).
• Caching: API calls with repeated context cost 90% less using prompt caching.
• Rate limits: enterprise tiers provide dedicated capacity with SLA guarantees.

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Evaluating AI Output Quality

Automated evaluation (LLM-as-judge, factuality checks, similarity scores) replaces manual review at scale. Building an eval suite is the difference between reliable AI and roulette.

• LLM-as-judge: use a separate AI to score outputs on rubric — correlation with humans is 80%+.
• RAGAS: open-source framework for evaluating RAG pipelines.
• Set a minimum quality threshold and test every model or prompt change against it.

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AI in Product Development

AI accelerates every phase: user research (synthesising interview transcripts), ideation (generating feature concepts), design (Figma AI), development (Copilot), testing (AI test generators), analytics (AI dashboards).

• Dovetail AI synthesises user research interviews in minutes.
• Figma AI generates UI mockups from natural language descriptions.
• AI test generators (GitHub Copilot, Testim) write unit tests from code automatically.

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Monitoring and Observability for AI Systems

AI systems in production need observability: latency tracking, cost monitoring, error rates, and output quality over time. Tools like LangSmith, Helicone, and Datadog AI Observability are essential.

• LangSmith traces every LLM call — invaluable for debugging multi-step agents.
• AI cost monitoring: set alerts at £500/month to catch runaway API usage.
• Eval regression: run your test suite after every model version update from your provider.