Type: Content Original link: https://x.com/karpathy/status/1937902205765607626?s=43&t=ANuJI-IuN5rdsaLueycEbA Publication date: 2025-09-23
Summary #
WHAT - Andrej Karpathy’s tweet promotes the concept of “context engineering” over “prompt engineering.” He argues that while prompts are short task descriptions for LLMs, context engineering is crucial for industrial applications, as it deals with effectively filling the context window of models.
WHY - It is relevant for AI business because it highlights the importance of advanced context management to improve the performance of language models in industrial applications. This can lead to more accurate and contextualized interactions with users.
WHO - Andrej Karpathy, an influential researcher and leader in the field of AI, is the author of the tweet. The AI community and LLM application developers are the main actors.
WHERE - It positions itself within advanced discussions on optimizing LLM applications, focusing on context engineering techniques to improve model performance.
WHEN - The tweet was published on 2024-01-05, indicating a current and relevant trend in the debate on optimizing language models.
BUSINESS IMPACT:
- Opportunities: Implementing context engineering techniques can significantly improve the performance of LLM applications, making them more accurate and contextualized.
- Risks: Ignoring the importance of context engineering could lead to less effective and less competitive LLM solutions in the market.
- Integration: Context engineering techniques can be integrated into the existing stack to optimize interactions with language models.
TECHNICAL SUMMARY:
- Core technology stack: Not specified in the tweet, but implies the use of advanced language models and context management techniques.
- Scalability and architectural limits: Effective context management can improve the scalability of LLM applications, but requires a deep understanding of the context window limitations of models.
- Key technical differentiators: Focus on context engineering can differentiate LLM applications, making them more robust and suitable for complex tasks.
Use Cases #
- Private AI Stack: Integration into proprietary pipelines
- Client Solutions: Implementation for client projects
- Strategic Intelligence: Input for technological roadmap
- Competitive Analysis: Monitoring AI ecosystem
Resources #
Original Links #
- +1 for “context engineering” over “prompt engineering” - Original link
Article suggested and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-09-23 17:17 Original source: https://x.com/karpathy/status/1937902205765607626?s=43&t=ANuJI-IuN5rdsaLueycEbA
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