LLM Post-Training 101 + Prompt Engineering vs Context Engineering | AI & ML Monthly
Published:Oct 13, 2025 03:28
•1 min read
•AI Explained
Analysis
This article from AI Explained provides a good overview of LLM post-training techniques and contrasts prompt engineering with context engineering. It's valuable for those looking to understand how to fine-tune and optimize large language models. The article likely covers various post-training methods, such as instruction tuning and reinforcement learning from human feedback (RLHF). The comparison between prompt and context engineering is particularly insightful, highlighting the different approaches to guiding LLMs towards desired outputs. Prompt engineering focuses on crafting effective prompts, while context engineering involves providing relevant information within the input to shape the model's response. The article's monthly format suggests it's part of a series, offering ongoing insights into the AI and ML landscape.
Key Takeaways
- •LLM post-training techniques are crucial for optimizing model performance.
- •Prompt engineering and context engineering offer different approaches to guiding LLMs.
- •AI Explained provides valuable insights into the AI and ML landscape.
Reference
“Prompt engineering focuses on crafting effective prompts.”