The Data Prep Kit (DPK) is an open-source toolkit developed by IBM Research to streamline the preparation of unstructured data for Large Language Model (LLM) applications. It addresses the challenges of processing diverse data types—such as text and code—for tasks like fine-tuning, instruction-tuning, and retrieval-augmented generation (RAG).
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tutorial2025-12-22
The article discusses the importance of memory in AI agents, particularly how it enables them to learn from past interactions and improve their performance over time. It categorizes memory into three types: session memory, user memory, and learned memory, each with distinct characteristics and benefits. The author provides code examples for implementing these memory types in agents, emphasizing the significance of learned memory for enhancing agent capabilities. The article concludes with a discussion on what constitutes a good learning and the need for human oversight in the learning process.
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vision2026-01-01
The article discusses the evolving landscape of data engineering as it adapts to the needs of AI agents in an increasingly automated environment. It emphasizes the importance of building reliable, code-first data platforms that can handle multimodal data and provide context for agents. The shift from traditional data engineering tasks to high-level system supervision is highlighted, along with the necessity for safety and correctness in data pipelines. Ultimately, the article envisions a future where humans and AI agents collaborate seamlessly, transforming data engineering practices.
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project2025-01-06
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