Three years of putting LLMs into Software - Lessons learned
What if LLMs are just a new, unreliable kind of API? Learn the critical lessons from three years of building real-world software with them.
#1about 4 minutes
Understanding the fundamental nature of LLMs
LLMs are unreliable pattern matchers that appear intelligent but lack true understanding, requiring developers to manage context and anticipate failures.
#2about 4 minutes
Controlling LLM output with API parameters
API parameters like temperature and top_p allow for control over the determinism and creativity of LLM responses by manipulating token selection probabilities.
#3about 7 minutes
Viewing LLMs as a new kind of API
LLMs should be treated as a new type of API for text manipulation, not as intelligent agents, because they are advanced pattern matchers with significant limitations.
#4about 5 minutes
Implementing practical LLM use cases in software
LLMs can be used for tasks like audio transcription, image analysis for OCR, and text reformulation by providing clear instructions and examples in the prompt.
#5about 4 minutes
Navigating legal compliance and data privacy
Using paid APIs with data privacy contracts, implementing human-in-the-loop workflows, and understanding the European AI Act are crucial for legal compliance.
#6about 2 minutes
Understanding the security risks of AI integrations
Integrating LLMs with external APIs or internal data creates significant security risks like prompt injection, requiring careful control over the AI's permissions and actions.
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