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Documentation Index

Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt

Use this file to discover all available pages before exploring further.

Welcome back to our deep dive into advanced Natural Language Processing (NLP) techniques powered by Large Language Models (LLMs). With GPT-3.5, GPT-4, and similar models, you can go well beyond simple text completion to perform a suite of tasks—such as summarization, sentiment analysis, translation, and formatting—using a single API endpoint.
You only need one LLM instance to handle multiple text-based tasks, reducing infrastructure complexity and accelerating development.
Before the rise of LLMs, each capability required its own specialized neural network. Today’s generative AI models are trained so broadly that you can prompt the same model to:
The image illustrates a large language model (LLM) at the center, with arrows pointing to its applications: summarization, translation, sentiment analysis, and formatting and conversion.
TaskDescriptionExample Prompt
SummarizationCondense long articles into key takeaways“Summarize the following report in three bullet points.”
Sentiment AnalysisDetect positive, neutral, or negative tone“Analyze the sentiment of this customer review.”
TranslationConvert text between multiple languages“Translate this paragraph from English to Spanish.”
Text FormattingReformat or convert markup, code, or prose“Convert this Markdown list into an HTML table.”

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Demo Walkthroughs

In the sections below, we’ll explore simple code examples using the OpenAI API Reference. Each demo shows how easily GPT-3.5 or GPT-4 can handle:
  1. Summarization
  2. Sentiment Analysis
  3. Translation
  4. Text Conversion
Be mindful of token usage and rate limits when processing large volumes of text. Review the OpenAI API pricing before running extensive jobs.

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