Prompt Engineering Guidelines

Warning

This guide is based on an early version of Magnus and includes certain limitations. Users are encouraged to verify the provided information for accuracy and relevance to their specific needs.

Note

PrimisAI has highlighted best practices to optimize Magnus' performance and to minimize these limitations. The guide presents the current limitations of Magnus, followed by common use cases and prompt engineering tips to achieve high-quality output.

Limitations

Hallucinations

Magnus, like many advanced language models, can occasionally generate information that is plausible-sounding but incorrect. This phenomenon, known as "hallucination," means that the model might produce answers or statements that do not align with factual data or the intended context. Users should verify critical information provided by Magnus and cross-reference it with trusted sources to ensure accuracy. This limitation highlights the importance of human oversight and the need for careful review of the model's outputs in high-stakes or sensitive applications.

Dependency on Quality and Clarity of Prompts

Magnus' performance is highly dependent on the quality and clarity of the prompts provided by the user. Vague or poorly structured prompts can lead to ambiguous or irrelevant responses. For optimal performance, users should provide detailed, specific, and well-structured prompts. This limitation underscores the importance of prompt engineering, where crafting precise questions or instructions can significantly enhance the relevance and accuracy of the model’s outputs.

Context Window

Magnus has a context window limit of 8096 tokens. This means that the model can process and retain up to 8096 tokens (words and characters combined) of input and output data at a time. When the input exceeds this limit, earlier content may be truncated or ignored, which can affect the model's ability to reference previous parts of the conversation or document accurately. Users should be mindful of this limitation and consider breaking down larger inputs into smaller, manageable chunks to ensure that all relevant information is considered by the model.

From Magnus Itself

Common Usage: General Hardware Knowledge

Common Usage: Code Analysis

Looking at the contents of the opened code window, Magnus can provide detailed insights on your current project.

Common Usage: RTL Code Generation

Prompting Magnus for RTL will provide a code block, easily paste-able into your code window.

Common Usage: Test Plan Generation

Magnus can assist with verification by outlining a comprehensive testing plan for a given module.

Common Usage: Testbench Code Generation

Following the developed testing plan, Magnus can convert use the plan into testbench code.

Prompt Engineering Tips

Requests to Magnus may often fall within the categories mentioned above. However, as Magnus is prone to hallucinations and errors, the following tips and guidelines can help steer Magnus in the right direction and achieve your goals.

By crafting well-designed prompts, you can get more accurate and relevant responses from the AI model.

All the tips mentioned below can be used together to enhance Magnus understanding of a given hardware task and produce quality RTL. Doing so builds Magnus awareness and improves the success rate of this next token prediction technology.

Prompt Engineering Tips: Providing Context

Context enables Magnus to comprehensively understand and effectively address the underlying goal.

Prompt Engineering Tips: Be Specific

Whether that be providing detailed specifications for a code generation prompt, or requesting a slight modification to existing code, articulating specifics typically results in more clear-cut responses from Magnus and removes ambiguity.

Prompt Engineering Tips: Request Re-Iteration

Asking Magnus to explain its understanding on a hardware design after you have explained it is an easy way for it to self-reinforce the concepts through articulation.

Prompt Engineering Tips: Operate Progressively

Working or revealing information progressively can manage complexity and guide Magnus through step-by-step reasoning.

Prompt Engineering Tips: Clarify As Needed

Often times, Magnus will not behave as expected, and user clarification will be required to get the conversation back on track.

Prompt Engineering Tips: Enforce "System 2" Thinking

System 1 thinking is a near-instantaneous process; it happens automatically, intuitively, and with little effort. It’s driven by instinct and experiences. On the other hand, System 2 thinking is slower and requires more effort. It is more conscious-like and logical. We can push Magnus into System 2 thinking through creative prompts. The example below demonstrates Magnus “thinking” how it should adjust RTL changes, before doing so. We’ve found this to be a powerful technique for higher quality results.