AI Tools for Productivity: Exploring Prompt Engineering and Key Features

Artificial Intelligence (AI) tools have revolutionized the software industry by streamlining data analysis, predictive analytics, natural language processing, image recognition, and automation of repetitive tasks. This enhances efficiency and supports better decision-making across a range of business processes.

Large Language Models (LLMs) are a breakthrough in AI, leveraging deep learning techniques and vast datasets to generate human-like text and perform complex natural language processing tasks. This presentation delves into how prompt engineering and other key features of AI tools contribute to productivity gains, highlighting their impact on various industries and offering insights into best practices for their implementation.

1. AI Tools for Productivity: Exploring Prompt Engineering and Key Features S M Nahid Hasan Junior Software Engineer Nascenia Ltd.

2. Outlines Introduction to AI Tools How LLM Works? Prompt Engineering Alternative AI tools Key Features

3. Introduction AI tools are used in software industry for tasks like data analysis, predictive analytics, natural language processing, image recognition, and automation of repetitive tasks, enhancing efficiency and decision-making. As a product As coding assistant As debugging tool As documentation RAG chatbot AI tools are being used-

4. Introduction of LLM A large language model is an advanced type of language model that is trained using deep learning techniques on massive amounts of text data and capable of generating human-like text and performing various natural language processing tasks. GPT Llama Mistral Popular LLMs T5 Alpaca Falcon

5. How LLM works? LLMs generate texts by predicting the next word in a sequence based on learned patterns from training data. They use attention mechanisms to understand and generate coherent and contextually relevant text.

6. Evaluation of LLM

7. Parameter and Token Size

8. Even Common Sense Has a Pattern LLMs capture common sense by learning from vast amounts of diverse internet data during training, enabling them to understand implicit relationships, general knowledge, and common-sense reasoning.

9. Even Common Sense Has a Pattern LLMs capture common sense by learning from vast amounts of diverse internet data during training, enabling them to understand implicit relationships, general knowledge, and common-sense reasoning.

10. Even Common Sense Has a Pattern LLMs capture common sense by learning from vast amounts of diverse internet data during training, enabling them to understand implicit relationships, general knowledge, and common-sense reasoning.

11. Prompt Engineering Prompt engineering involves designing effective prompts or input formats to elicit desired responses from LLM. It entails crafting specific instructions, questions, or context to guide the model's generation process and achieve desired outcomes.

12. Types of Prompting Zero Shot One Shot Few Shot Chain of Thought Iterative Negative Hybrid

13. Zero Shot Prompting The task is given to the AI without any prior examples. Detailed descriptions are provided, assuming the AI has no prior knowledge of the task.

14. One Shot Prompting One-shot prompting involves requesting a response from a language model with minimal context. One example is provided along with the prompt, LLM understands the context or format what is expected.

15. Few Shot Prompting This process involves providing a few examples (usually 2–5) to assist the LLM in understanding the pattern or style of the expected response.

16. Chain of Thought(CoT) Prompting LLM is instructed to articulate its sequential thought process, providing step-by-step explanations. This method is valuable for tackling intricate reasoning tasks, enabling transparency in the LLM’s decision-making logic and enhancing interpretability of its outputs.

17. Iterative Prompting Iterative prompting involves refining the prompt based on the outputs received, gradually guiding the AI toward the desired answer or style of response. Throughout this process, adjustments are made to the prompt in response to the AI's outputs, facilitating the attainment of specific objectives or response qualities.

18. Negative Prompting In negative prompting, instructions are provided to the AI regarding what should be avoided in the response. This method specifies exclusions or limitations on the type of content expected, guiding the AI to generate responses that adhere to predefined constraints or preferences.

19. Hybrid Prompting Hybrid prompting involves the combination of various methods, such as integrating few-shot with chain-of-thought approaches, to achieve more precise or creative outputs. This blending of techniques allows for enhanced flexibility and adaptability in generating diverse responses. Chain of Thought + One shot Prompt Iterative + Chain of Thought Prompt Chain of Thought + Negative Prompt

20. Alternative Tools GitHub Copilot DataLab AI Assistant Codeium Blackbox AI Code GPT Cody Tabnine Replit AI

21. Documentation AI tools can help in documentation.

22. Refactoring Code AI tools can help in refactoring code.

23. Lexical Search Lexical search is a type of search that matches exact words or phrases in text, focusing on syntactic patterns without considering the contextual meaning or semantics of the words.

24. Semantic Search Semantic search is an advanced method of retrieving information that understands the meaning behind words in a search query to provide more accurate and contextually relevant search results.

25. Thank you