𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝘁 𝗔𝗜 𝗣𝗮𝗿𝗮𝗱𝗶𝗴𝗺𝘀: 𝗔 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸
Six foundational paradigms are redefining intelligent system development. Here's the essential knowledge architecture.
Six foundational paradigms are redefining intelligent system development. Here's the essential knowledge architecture.
Master data cleaning and feature engineering. Use classical ML algorithms effectively alongside deep learning frameworks. Focus on hyperparameter tuning and experiment logging for reproducible results.
𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
Build systems that assign specific agent roles and manage tool registries efficiently. Create event driven and reactive programming patterns. Track memory and context states while handling user interactions seamlessly.
𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
Implement chunking strategies for large data. Generate embeddings using open libraries. Master vector database querying and multi source retrieval via unified APIs. Construct and evaluate prompt templates systematically.
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
Design goal oriented intelligent agents with episodic and long term memory. Break down task decomposition and scheduling techniques. Perfect agent communication and coordination with feedback and self evaluation loops.
𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀
Master zero shot and role based prompting. Understand tokenization and embedding techniques. Apply fine tuning with LoRA and PEFT. Optimize model context windows and mitigate hallucination in output.
𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
Design AI workflows from prompt to tool to memory. Handle model deployment and version control. Implement serverless LLM integration and build robust CI/CD for AI pipelines. Optimize for cost and latency.
𝗖𝗼𝗿𝗲 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: TensorFlow, PyTorch, LangChain, Pinecone, Chroma, ChatGPT, Claude, Gemini, OpenAI, Hugging Face, FastAPI, Docker, Vercel.
