We’ve just launched a brand-new 8-hour Generative AI Primer designed to help developers master, use, and build with LLMs. Why did we build this course? Over the last few years, I’ve seen a huge gap between AI theory and real-world application—especially in the fast-moving LLM
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Learning RAG, LLMs, AI Agents, and ML/AI Engineering
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Want to learn more about: – RAG – LLMs – AI Agents – ML/AI Engineering Find me →
@akshay_pachaar -
Tool Use Pattern: LLM Brain with Tools as Hands for Actions
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Tool Use Pattern Imagine the tool use agent as a person, with the LLM as its brain and a set of tools as its hands to take actions. By querying databases, calling APIs, and executing Python scripts, the LLM moves beyond relying solely on its internal knowledge.
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Reflection Pattern: AI Self-Critique and Iterative Refinement Cycle
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Reflection Pattern In this pattern, the AI “reviews” its own work—spotting mistakes and iterating until the final outcome is polished. Think of it like a self-critique cycle where the model refines each draft before presenting the result!
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Popular Design Patterns for Building AI Agents
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Agents allow a LLMs to repeatedly refine their output by incorporating self-evaluation, planning, and collaboration! Today, we'll understand some popular design patterns employed in building with AI agents. Let's go!
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Top 5 Agentic AI Design Patterns Clearly Explained
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Top 5 Agentic AI design patterns, clearly explained (with visuals):
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Using Cursor for automated coding tasks
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Cursor aurait mis 5min moi je code plus c’est bon c’est les IA qui s’en chargent ahah
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Singular Value Finetuning Method for Self-Adaptive LLMs
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We also provide example scripts for training and evaluation, for playing around with the Singular Value Finetuning (SVF) method introduced in Transformer² We hope this will be useful for researchers and developers interested in self-adaptive LLMs.
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o3-mini Developer Features Match o1-mini Capabilities
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Yes, o3-mini will have the same developer features as o1-mini—including function calling and structured outputs.
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Pre-training Foundations: Self-supervised Learning and LLM Architectures
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2/ Chapter 1: Pre-training Foundations
Demystify self-supervised learning
Explore architectures like encoder-decoder models
See how pre-training powers scalability in AI Master the backbone of LLMs like BERT!