Text Summarization with LLMs in Python

     In this post, we'll briefly learn what text summarization is, how large language models approach it, and how to build practical summarization pipelines for different document types and length constraints in Python. The tutorial covers:

1. What is Text Summarization?
2. Types of Summarization
3. Installation and Setup
4. Basic Summarization with a System Prompt
5. Controlling Summary Length and Style
6. Bullet-Point and Structured Summaries
7. Multi-Document Summarization
8. Chunked Summarization for Long Documents
9. Choosing the Right Summarization Approach
10. Conclusion

 Let's get started.

Few-Shot Prompting with LLMs in Python

In this post, we'll briefly learn what few-shot prompting is, how it works, and how to apply it to real-world NLP tasks to produce more accurate and consistent outputs from a large language model in Python. The tutorial covers:

1. What is Few-Shot Prompting?
2. How Few-Shot Prompting Works
3. Installation and Setup
4. Zero-Shot vs Few-Shot Comparison
5. Few-Shot Text Classification
6. Few-Shot Named Entity Extraction
7. Few-Shot Structured JSON Output
8. Few-Shot Style Transfer
9. Choosing the Right Number of Examples
10. Conclusion

Let's get started.

How to Limit LLM Response Length with Max Tokens in Python

In this post, we'll briefly learn what max tokens means in the context of large language models, how it controls the length of generated responses, and how to set it effectively for different tasks in Python. The tutorial covers:

1. What are Max Tokens?
2. How Tokens are Counted
3. Installation and Setup
4. Setting Max Tokens for Short Responses
5. Setting Max Tokens for Long Responses
6. Detecting a Truncated Response
7. Max Tokens for Structured Output Control
8. Estimating Token Count Before Sending
9. Choosing the Right Max Tokens Value
10. Conclusion

Let's get started.

How to Use Top-P and Top-K Sampling in LLMs

In this post, we'll briefly learn what Top-K and Top-P sampling are, how they differ from temperature, and how to tune them to control the quality and diversity of LLM output in Python. The tutorial covers:

1. What are Top-K and Top-P Sampling?
2. How Top-K Sampling Works
3. How Top-P Sampling Works
4. Installation and Setup
5. Effect of Top-K on Output
6. Effect of Top-P on Output
7. Comparing Top-K and Top-P Directly
8. Combining Temperature, Top-K, and Top-P
9. Choosing the Right Sampling Parameters
10. Conclusion

Let's get started.

How to Control LLM Output Randomness with Temperature in Python

In this post, we'll briefly learn what temperature is in the context of large language models, how it controls the randomness of generated text, and how to set it correctly for different tasks in Python. The tutorial covers:

1. What is Temperature?
2. How Temperature Works
3. Installation and Setup
4. Comparing Temperature Values Side by Side
5. Low Temperature for Factual and Structured Tasks
6. High Temperature for Creative Tasks
7. Temperature and Top-p Sampling
8. Choosing the Right Temperature
9. Conclusion

Let's get started.

How to Use System Prompts to Control LLM Behavior

In this post, we'll briefly learn what a system prompt is, why it is the most powerful lever for controlling LLM behaviour, and how to craft effective system prompts for a variety of real-world scenarios in Python. The tutorial covers:

1. What is a System Prompt?
2. How System Prompts Work
3. Installation and Setup
4. Setting Tone and Persona
5. Constraining the Output Format
6. Restricting the Topic Domain
7. Controlling Response Length and Style
8. Chaining System and Few-Shot Prompts
9. Conclusion

Let's get started.