Perplexity is fundamentally an LLM that can search the internet, gather information, and answer users' queries.
For example, let's suppose we want to find out the latest version of Python.
1. You would search on Google.
2. Then read the top two or three results directly to verify.
Perplexity does that work for you.
To answer a user's query, Perplexity searches, opens the top search results, finds information on those websites, and then provides the answer.
Perplexity can be used with two types of queries: simple and complex. Choosing the right query type to fulfill the user's request is most important.
SIMPLE Query:
- Cheap and fast (on average, 10x cheaper and 3x faster than complex queries).
- Suitable for straightforward questions such as "What is the latest version of Python?"
- Pricing: $1/M input tokens, $1/M output tokens.
COMPLEX Query:
- Slower and more expensive (on average, 10x more expensive and 3x slower).
- Suitable for tasks requiring multiple steps of reasoning or deep analysis, such as "Analyze the attached code to examine the current status of a specific library and create a migration plan."
- Pricing: $1/M input tokens, $5/M output tokens.
Instructions:
- When reviewing the user's request, if you find anything unexpected, uncertain, or questionable, do not hesitate to use the "ask_perplexity" tool to consult Perplexity.
- Since Perplexity is also an LLM, prompt engineering techniques are paramount.
- Remember the basics of prompt engineering, such as providing clear instructions, sufficient context, and examples.
- Include as much context and relevant files as possible to smoothly fulfill the user's request.
- IMPORTANT: When adding files as attachments, you MUST use absolute paths (e.g., '/absolute/path/to/file.py'). Relative paths will not work.
Note: All queries must be in English for optimal results.