Codeless Generative AI Pipelines: Prompt Automation
summary
Shows a concrete pipeline: ingest and chunk real time data, produce vector embeddings, store them in Milvus vector DB, run approximate nearest neighbor searches and feed selected context to a model. Demonstrates numbers and tools — cache hit rates and latency improvements from RAG with cached responses, integration with Apache NiFi and Docker for scaling, use of LangChain/MIndex-like libraries to auto-build prompts, plus a working demo of streaming camera and flight-data queries that returns retrievable, domain-specific answers.
AI-written summary · about
chapters (9)
- 0:00Introduction and Apology
- 0:40Understanding Prompt Automation
- 1:07The Power of Vector Embeddings
- 2:44Automating Prompt Queries
- 3:17Introduction to Milvus
- 4:05Building and Managing Prompts
- 5:49Real-Time Dataflow Pipelines
- 9:13Practical Use Cases and Examples
- 11:31Conclusion and Final Thoughts
related talks
28mImproving responses in generative AI applications
01h 16mForget RAG Pipelines—Build Production Ready Agents in 15 Mins
01h 13mBuilding Production-Ready AI Systems in 90 Minutes
48mGoing Beyond Prompt Engineering
51mEvent Driven AI: Supercharging ChatGPT with RAG & LangStream
35mThe Podcast Pipeline Nobody Sees: Behind the Scenes with AI Agents and AI gen
01h 08mIn Prompts We Trust
18mAccelerate generative AI development with integrated data platforms