Prompt engineering is the discipline of creating and optimizing prompts for generative AI with a focus on cost efficiency for business applications.
A well-structured prompt for enterprise use includes a clear instruction, input data, context, a specified output format, and examples.
Generative AI models process prompts as tokens, and costs are based on the number of input and output tokens, making model selection a key financial decision.
Reduce costs by optimizing data formats, managing white spaces, using tabular data, and leveraging tools like LLM Lingua to compress prompts.
Improve model output by providing clear examples, using the Chain of Thought technique for reasoning, and adjusting API parameters like temperature.
Process data larger than the context window using prompt chunking and reduce false information by giving the model an out or telling it not to invent facts.
The RAG pattern combats hallucinations by dynamically retrieving relevant data from enterprise sources and injecting it directly into the prompt as context.
Frameworks like LangChain and Semantic Kernel provide abstractions for interacting with LLMs, while tools like Prompt Flow support the entire LLM Ops lifecycle.
The role of prompt engineering is evolving, raising questions about whether it will become a specialized job or an essential skill for all developers.
32:26Krzysztof Cieślak
25:17Scott Hanselman
25:03Alla Pavlova
31:12Daniel Töws
27:10Georg Dresler
26:30Simon A.T. Jiménez
24:05Nimrod Kor
26:54Duan Lightfoot
Jobs that call for the skills explored in this talk.
Conrad Electronic SE