Prompt Engineering fundamental course | Prompt Engineering for Technical Applications (Code, Math, Data) by Diljeet Singh | Learn Smarter
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Prompt Engineering for Technical Applications (Code, Math, Data)

Prompt engineering enhances the capabilities of AI in various technical domains, including coding, mathematics, and data analysis. By using structured prompts, individuals can generate, debug, and explain code, as well as approach mathematical and logical problems systematically. Additionally, the techniques can also streamline interactions with data and perform SQL-like queries efficiently.

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Sections

  • 8

    Prompt Engineering For Technical Applications (Code, Math, Data)

    This section explores the application of prompt engineering in generating code, solving mathematical problems, and analyzing data with AI assistance.

    Learning Practice

  • 8.1

    The Role Of Ai In Technical Domains

    This section discusses how AI can assist with technical tasks such as writing code, solving mathematical problems, debugging, simulating queries, and analyzing data.

    Learning Practice

  • 8.2

    Coding With Prompts

    This section discusses how to effectively use prompts for code generation, debugging, and technical explanations using language models.

    Learning Practice

  • 8.2.1

    Basic Code Generation

    This section discusses how to use prompt engineering for generating basic code, specifically focusing on structured and precise prompts for effective coding.

    Learning Practice

  • 8.2.2

    Multistep Instruction

    This section covers how to use multistep instructions in prompt engineering to develop detailed code tasks.

    Learning Practice

  • 8.2.3

    Debugging And Fixes

    This section outlines how to utilize prompt engineering for debugging code by identifying errors and suggesting fixes.

    Learning Practice

  • 8.3

    Explaining Code

    This section focuses on the use of prompt engineering to explain code systematically, especially tailored for different audience levels.

    Learning Practice

  • 8.4

    Mathematical Problem Solving

    This section focuses on effective prompting techniques for solving mathematical problems using AI.

    Learning Practice

  • 8.5

    Advanced Math And Logic

    In this section, learners will explore how to utilize prompt engineering for advanced mathematical inquiries and logical reasoning.

    Learning Practice

  • 8.6

    Data Analysis & Sql Simulation

    This section covers how language models can simulate SQL and spreadsheet logic for data analysis, demonstrating skills through practical example prompts.

    Learning Practice

  • 8.7

    Spreadsheet & Csv Tasks

    This section focuses on utilizing prompts to create and analyze spreadsheet formulas and CSV data while enhancing understanding of essential spreadsheet tasks.

    Learning Practice

  • 8.8

    Data Summarization

    The section covers how to effectively summarize data using prompt engineering.

    Learning Practice

  • 8.9

    Best Practices For Technical Prompts

    This section highlights the essential strategies for crafting effective technical prompts in AI to ensure accurate and informative outputs.

    Learning Practice

  • 8.10

    Handling Model Limitations

    This section outlines strategies to effectively manage limitations inherent in AI models during technical applications.

    Learning Practice

References

Chapter 8_ Prompt Engineering for Technical Applications .pdf

Class Notes

Memorization

What we have learnt

  • Prompt engineering aids in ...
  • Precise instructions improv...
  • Best practices in prompt de...

Final Test

Revision Tests