𝗜𝗻𝘁𝗿𝗼 𝘁𝗼 𝗚𝗲𝗻 𝗔𝗜 𝗳𝗼𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀
Most developers write code using fixed rules. You take input, apply if/else statements or loops, and return a predictable result. This works for structured data like JSON or CSV.
But what about messy tasks?
- Summarizing 100 support tickets.
- Turning a feature list into an email.
- Comparing pros and cons from blog posts.
You could use regex or complex templates, but that is fragile. Generative AI handles this fuzzy work easily.
How it works
A generative model follows one simple loop:
- It looks at your prompt and previous text.
- It predicts the next small piece of text.
- It adds that piece to the text.
- It repeats this until the task is done.
Large Language Models (LLMs) use an architecture called a transformer. Instead of reading left to right, the model looks at all parts of your input at once. It decides which words matter most for the next prediction.
Two things matter for Python developers: tokens and context windows.
Tokens Models do not read raw strings. They break text into tokens. A token is a small chunk of a word. API pricing and limits use tokens instead of characters.
Context Window This is the amount of text the model sees at one time. It includes your prompt, the chat history, and the model output. If you exceed this limit, the model forgets the oldest parts of the conversation.
Moving from prompts to agents
Sending a single prompt to a model is a start. You can summarize text or explain errors. However, a basic LLM has limits:
- It cannot call your databases or APIs.
- It does not remember things between requests.
- It can invent facts.
To build real products, you need agents. An agent adds three things to the LLM:
- Tools: Functions like web search or calculators.
- Memory: Storage for past conversations and user data.
- Orchestration: The logic to decide which tool to use and when to stop.
A plain LLM gives one answer. An agent takes a goal and uses tools and memory to reach a reliable result.
Optional learning community: https://t.me/GyaanSetuAi