1. Latent Space and Language Models:
- Latent space refers to the encoded knowledge of language in a model, stored as patterns from training on large databases like RefinedWeb, Common Crawl, Wikipedia, StarCoder data, and more.
In other words, latent space refers to a high-dimensional space where the model represents the underlying features of data in a compressed, abstract form. - When you prompt a language model, it searches its latent space for relevant knowledge and generates output by assembling statistics into words.
- If a prompt is dissatisfactory, the model might be missing the necessary information in its latent space, causing it to hallucinate (generate incorrect but plausible-sounding responses).
2. Hallucinations and Model Limitations:
- Smaller models or those not fine-tuned may lack enough knowledge on a given topic, leading to hallucinations.
- Models generate responses based on statistical likelihood and don’t reason logically.
- Prompt engineering involves understanding these limitations and designing prompts accordingly to avoid generating hallucinations.
3. Key Techniques in Prompt Engineering:
- Be specific: Include clear instructions, format, examples, tone, output length, and context.
- Provide examples: Offer sample texts, data formats, templates, or visuals.
- Experiment iteratively: Test prompts and adjust based on results.
- Know the model’s strengths and weaknesses: Understand what your model excels at and where it may fail.
- Balance simplicity and complexity: Avoid vague prompts to prevent irrelevant or unexpected answers.
- Use multiple comments for context: Provide more background without cluttering the prompt.
- Add guardrails: Ensure safety and privacy by setting filters and defining unwanted topics.
4. Prompt Engineering Risks and Limitations:
- Prompt Injection: Manipulating the model to produce malicious responses.
- Jailbreaking: Bypassing safety measures (guardrails).
- Hijacking: Manipulating the original prompt with new instructions.
- Poisoning: Embedding harmful instructions in various inputs.
- Guardrails: Set rules to filter out harmful, offensive, or sensitive inputs and block unsafe content.
5. AWS Services for Prompt Engineering:
- Amazon Bedrock and Amazon Titan offer pre-trained models that can be customized through prompt engineering.
- They provide tools and APIs for refining prompts, monitoring outputs, and building applications like content creation, summarization, question answering, and chatbots.
