The foundation of every Claude integration — simple request/response.
from anthropic import Anthropic
client = Anthropic()
# reads ANTHROPIC_API_KEY from env
# Simple message
response = client.messages.create(
model="claude-sonnet-4-5",
max_tokens=1024,
system="You are a helpful FDE assistant.",
messages=[{"role": "user", "content": "Explain RAG in 2 sentences"}]
)
print(response.content[0].text)Complete lifecycle of large language models from pre-training through fine-tuning, RLHF, and deployment — with architecture diagrams and production considerations.
When and how to fine-tune LLMs: LoRA, QLoRA, full fine-tuning decision framework with cost analysis and real-world examples.
How LLMs use function calling and tool use — the mechanics behind tool-calling agents, from prompt engineering to structured output.
Step-by-step breakdown of an LLM tool call: request, schema validation, execution, and result handling with code examples.
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