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
1. Zero Shot
2. One Shot
3. Few Shot
4. Chain of Thought
5. Iterative
6. Negative
7. 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
1. GitHub Copilot
2. DataLab AI Assistant
3. Codeium
4. Blackbox AI
5. Code GPT
6. Cody
7. Tabnine
8. 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