Maxim Salnikov
Prompt Engineering - an Art, a Science, or your next Job Title?
#1about 5 minutes
Defining prompt engineering for developers
Prompt engineering is the discipline of creating and optimizing prompts for generative AI with a focus on cost efficiency for business applications.
#2about 2 minutes
Understanding the core components of a prompt
A well-structured prompt for enterprise use includes a clear instruction, input data, context, a specified output format, and examples.
#3about 5 minutes
How tokens and model selection impact cost
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.
#4about 4 minutes
Practical strategies for reducing token count
Reduce costs by optimizing data formats, managing white spaces, using tabular data, and leveraging tools like LLM Lingua to compress prompts.
#5about 6 minutes
Core prompting strategies and techniques
Improve model output by providing clear examples, using the Chain of Thought technique for reasoning, and adjusting API parameters like temperature.
#6about 5 minutes
Managing large inputs and mitigating hallucinations
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.
#7about 2 minutes
Grounding models with retrieval-augmented generation (RAG)
The RAG pattern combats hallucinations by dynamically retrieving relevant data from enterprise sources and injecting it directly into the prompt as context.
#8about 2 minutes
Exploring frameworks and tools for LLM development
Frameworks like LangChain and Semantic Kernel provide abstractions for interacting with LLMs, while tools like Prompt Flow support the entire LLM Ops lifecycle.
#9about 3 minutes
Considering the future of prompt engineering
The role of prompt engineering is evolving, raising questions about whether it will become a specialized job or an essential skill for all developers.
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