Introduction
What is "Prompt Engineering" ?
To understand, pay attention on the Green highlighted words
Prompt engineering is the process of designing and refining prompts to guide the behavior of large language models (LLMs) and other generative AI tools to elicit the desired output from the model. It is an iterative process of experimenting with different prompts to see how they affect the model's output, and then using that knowledge to refine the prompts further.
What is "LLM" ?
Large Language Models (LLMs) are a subclass of Artificial Intelligence (AI) adept at understanding and generating human language. Their monumental size, often characterized by billions of parameters, empowers them to learn intricate relationships between words and concepts, leading to a wider range of capabilities compared to traditional language models.
Key characteristics of LLMs:
Scalability: Their massive parameter count enables them to tackle complex tasks with higher accuracy and nuance.
Versatility: LLMs handle diverse language-related tasks, including:
Text Generation: Producing different text formats like poems, code, scripts, musical pieces, and various communication forms.
Text Comprehension: Analyzing text to understand its meaning, answer questions, and provide summaries.
Translation: Efficiently translating text between languages.
Dialogue: Engaging in human-like conversations and responding to prompts in a conversational manner.
Training: LLMs undergo rigorous training on vast text datasets using self-supervised and semi-supervised learning techniques.
Examples: Prominent LLM examples include Gemini, GPT-4, LLaMA, Phi-2 etc..
What is "Agent" ?
An Agent in the Agentic Framework is an autonomous, intelligent entity capable of perceiving its environment, making decisions, and executing tasks to achieve specific goals. It operates based on predefined objectives, contextual understanding, and learned experiences while adapting to changing scenarios through continuous feedback.
Key Characteristics of an Agent:
Autonomy:
Operates independently without constant human supervision.
Makes decisions based on internal rules, knowledge, and real-time data.
Perception:
Gathers information from its environment through sensors, APIs, or external data sources.
Understands and interprets this data using natural language processing (NLP), computer vision, or other modalities.
Knowledge:
Maintains contextual memory by storing historical data, facts, and rules in a knowledge base.
Uses contextual retrieval and semantic search for informed decision-making.
Reasoning & Decision-Making:
Uses inference engines, decision rules, or machine learning models.
Balances logical reasoning and probabilistic predictions.
Action Execution:
Performs tasks like sending messages, triggering workflows, or controlling external systems.
Works in a task-oriented or goal-driven manner.
Learning & Adaptation:
Continuously improves by learning from past interactions and feedback.
Incorporates reinforcement learning, supervised learning, or fine-tuning mechanisms.
Interaction & Collaboration:
Communicates with users, other agents, or systems through natural language or structured APIs.
Can operate in a multi-agent system, collaborating to achieve complex objectives.
Ethics & Security Compliance:
Adheres to ethical guidelines, privacy standards, and secure operational practices.
Incorporates explainability, transparency, and bias mitigation strategies.