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Prompt Engineering for Legal Work
Skip the Training, Reap the Rewards: How Legal Teams Can Adopt AI Effortlessly
How AI-driven legal tools are transforming adoption challenges into effortless productivity.
Few-shot learning | Best practices for legal prompt engineering
Few-shot learning helps LLMs deliver better outputs with just a few examples -- no fine-tuning necessary
Use separators to divide prompts | Best practices for legal prompt engineering
Use headers, special characters, XML tags, or system messages as separators to improve prompt clarity and guide LLMs to deliver more accurate, structured responses.
Structuring LLM outputs | Best practices for legal prompt engineering
Craft prompts to control LLM output formats for seamless use in databases, spreadsheets, or downstream applications.
Prompt | Elements of prompt engineering
Effective prompt design is key to guiding LLMs to generate accurate, structured responses.
Model Temperature | Elements of prompt engineering
Learn how to configure an LLM's temperature setting to optimize for consistency or creativity.
Choice of Model | Elements of prompt engineering
Choosing the right large language model depends on balancing cost, capability, and speed for your specific needs.
Maximum Length | Elements of prompt engineering
Learn how to control the maximum length of prompt outputs and structure your prompts to prevent context window failures.
System Message versus User Message | Elements of prompt engineering
Understand the difference between a system message and user message.
