Pragmatic AI: Reshaping the Product Opportunity Tree
summary
Company shifts from costly ML proofs to LLM-driven features after November 2022, building a tiny internal platform: a one-endpoint API that pulls prompts from a prompt catalog on GitHub, JavaScript SDK, chat and Slackbot integrations, and per‑prompt EML configs. Implementation effort falls nearly to zero and monthly API spend sits around €200 during heavy internal use. Remaining problems are model volatility, production quality checks, knowledge‑base hygiene, prompt engineering and routing to specialty models.
AI-written summary · about
chapters (8)
- 0:00Introduction and background
- 1:09Early learning analytics project
- 4:51Shift to generative AI
- 6:18Building internal AI service
- 12:20Technical implementation details
- 16:39Challenges with AI adoption
- 23:19Organizational and skill changes
- 25:28Conclusion and Q&A
related talks
01h 39mMastering LLMs: From Prompt Engineering to Agentic AI
26mMastering Large Language Models: Prompt Engineering for Precision & Performance
06mPrompt Engineering a Product Manager
14mEdge of the Present: Pursuing AI’s Frontier in the Enterprise
25mHuman-Centered AI: What I've Learned From Building Two LLM-Powered Products Thi...
46mPractical End-to-End AI Development using Prompty and AI Studio
40mCode-First LLMOps from prototype to production with GenAI tools
43mBuild and Maintain your Company Copilot with Azure ML and GPT-4 Q&A