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World Congress 2024 • Aug 20, 2024 • Session details

Prompt Engineering - an Art, a Science, or your next Job Title?

Maxim Salnikov

Enterprise prompt engineering goes beyond casual chat. Master token optimization, defeat hallucinations with RAG, and use frameworks like LangChain to build scalable, cost-effective AI applications.

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#1 about 6 min

Defining prompt engineering across personal and enterprise contexts

The widespread use of generative models spans productivity, creativity, and structured application development.

#2 about 3 min

Enterprise prompt anatomy and essential structural components

Structuring instructions with explicit data formats and targeted examples improves enterprise API outcomes.

#3 about 5 min

How language models process tokens and calculate costs

Understanding tokenization enables developers to manage API expenses and establish effective model selection strategies.

#4 about 2 min

Strategies for optimizing token usage in API calls

Formatting tabular data and monitoring whitespace can significantly reduce the overall token payload.

#5 about 2 min

Using language models to compress prompt payloads programmatically

Tools like LLM Lingua shrink prompt lengths while retaining essential meaning to lower API costs.

#6 about 3 min

General best practices for clear and effective instructions

Explicit syntax, isolated tasks, and parameter adjustments like temperature enhance model reliability.

#7 about 2 min

Improving model categorization accuracy using targeted prompt examples

Injecting specific few-shot examples into the prompt resolves edge cases in classification tasks.

#8 about 2 min

Fixing calculation errors with chain of thought reasoning

Prompting models to work step-by-step prevents logic shortcuts and produces accurate mathematical answers.

#9 about 2 min

Splitting large data inputs to manage context window limits

Prompt chunking allows applications to summarize extensive documents by parallelizing multiple smaller requests.

#10 about 3 min

Mitigating model hallucinations in enterprise generative applications

Providing explicit boundaries and fallback responses restricts the model from inventing false information.

#11 about 3 min

Injecting enterprise data using retrieval augmented generation patterns

The RAG architecture fetches proprietary data dynamically to anchor model responses in absolute factual truth.

#12 about 2 min

Developer frameworks for enterprise large language model operations

Libraries like LangChain, Semantic Kernel, and Prompt Flow streamline orchestration and monitoring of generative systems.

#13 about 3 min

Exploring the future evolution of prompt engineering roles

As frameworks abstract complexity and models become multimodal, prompting skills will transition into essential developer knowledge.

Matching moments

1:31 min

The shift from human prompt engineering to AI-generated prompts

Vitaly Friedman Vitaly Friedman · World Congress 2026 Europe

4:56 min

Building agentic workflows using prompt engineering and language models

1:46 min

Refining complex software instructions using automated prompt engineers

Markus Walker Markus Walker · World Congress 2023

1:44 min

Basics of generative AI and prompt interactions

Cheuk Ho · World Congress 2023

3:08 min

Building internal prompt libraries to optimize context engineering

Julia Kordick Julia Kordick · Coffee With Developers

5:31 min

Steering model behavior through effective prompt engineering techniques

Krzysztof Cieślak Krzysztof Cieślak · World Congress 2024

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