Day 2 - Jan 22 2026

Python Essentials for GenAI

1. The Core Data Structures

In GenAI engineering, we don't just use variables; we structure data to feed into models.

A. Lists (The Sequence)

• What it is: An ordered collection of items.

• GenAI Context: Lists are used to store Conversation History. An LLM needs to "remember" previous turns in a conversation, so we store messages in a list.

• Code Snippet:

  # A list of conversation turns
  chat_history = [
      "User: Hello", 
      "AI: Hi there! How can I help?", 
      "User: Explain Quantum Computing"
  ]

B. Dictionaries (The Structure)

• What it is: A collection of Key-Value pairs. This is the most critical structure for GenAI.

• GenAI Context: LLM APIs (like OpenAI or DeepSeek) expect messages in a specific dictionary format, usually containing a role and content.

• Code Snippet:

  Python

  # A single message object formatted for an LLM
  message = {
      "role": "user",
      "content": "Write a poem about Python."
  }

2. File Handling (Input/Output)

AI Agents need to read prompts from files and save their generated outputs. We avoid hard-coding text inside scripts.

• The with Statement: We use with open(...) because it automatically closes the file even if errors occur. This is crucial for long-running AI agents to prevent memory leaks.

• Modes:

  • 'r' (Read): To load prompt templates or configuration text.

  • 'w' (Write): To save logs or AI responses (overwrites existing).

  • 'a' (Append): To add new logs to an existing file without deleting history.

# Reading a prompt template
with open('prompt.txt', 'r') as f:
    prompt = f.read()

# Saving the AI's output
with open('output_log.txt', 'a') as f:
    f.write("New Entry: Operation Successful\n")

3. JSON: The Language of LLMs

JavaScript Object Notation (JSON) is the universal standard for data exchange. While we write in Python, LLMs speak JSON.

• Why it matters: When you send a request to ChatGPT or Claude via code, you are sending a JSON payload. When the model replies, it sends back JSON.

• Key Functions:

  • json.dumps(): Converts a Python Dictionary ➝ JSON String (for sending).

  • json.loads(): Converts a JSON String ➝ Python Dictionary (for parsing).

Tactical Example:

import json

# Python Dictionary (Internal use)
data = {"model": "gpt-4", "temperature": 0.7}

# Convert to JSON string (To send to API)
json_payload = json.dumps(data) 
print(json_payload) 
# Output: '{"model": "gpt-4", "temperature": 0.7}'

4. Modular Functions

Writing monolithic code (one huge block) is bad practice. In GenAI, we create Helper Functions to handle repetitive tasks.

• Example Use Case: You might write a function called get_embedding(text) or save_to_vector_db(data). This keeps the main logic clean and readable.

def log_mission(agent_name, status):
    """
    A reusable function to log agent status.
    """
    entry = f"Agent: {agent_name} | Status: {status}"
    print(entry)
    return entry

# Calling the function
log_mission("007", "Active")

Key Learnings:

• Why JSON? Large Language Models (LLMs) interact best via JSON. Learning to parse JSON is critical for building AI Agents.

• Data Structures: Used Lists to hold the collection of data and Dictionaries to represent individual data points (Key: Value).

• File I/O: Implemented open() with with statements to safely read raw text and write structured data.

Status:

• Input File Created (mission_data.txt)

• Processing Script Executed (day2_essentials.py)

• JSON Output Generated