The LLM Engineer

LLM Engineering Concepts

Understanding key concepts in Large Language Models (LLMs), Agents, OpenAI, RAG, and Fine-Tuning.

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1. Introduction to LLMs

Large Language Models (LLMs) process text using deep learning. Examples include OpenAI's GPT, Claude, LLaMA, and Falcon.

from openai import OpenAI
client = OpenAI(api_key="your_api_key")

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What is LLM?"}]
)
print(response.choices[0].message["content"])

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2. Retrieval-Augmented Generation (RAG)

Enhances LLMs by integrating retrieval mechanisms using vector databases like FAISS, Pinecone, and Weaviate.

import faiss
import numpy as np

d = 128  # Vector dimension
index = faiss.IndexFlatL2(d)

data = np.random.random((100, d)).astype('float32')
index.add(data)

query = np.random.random((1, d)).astype('float32')
D, I = index.search(query, 5)
print(I)  # Indices of closest vectors

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3. Fine-Tuning LLMs

Fine-tuning is training a pre-trained model on domain-specific data.

openai api fine_tunes.create -t "training_data.jsonl" -m "gpt-3.5-turbo"

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4. Agents in LLMs

Agents use planning & tool execution to complete tasks. Example using LangChain:

from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools import Tool

tools = [Tool(name="Calculator", func=lambda x: eval(x), description="Performs math operations.")]

agent = initialize_agent(tools, OpenAI(model="gpt-4"), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What is 10 + 25?")

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5. Vector Databases & Embeddings

from openai import OpenAI

client = OpenAI(api_key="your_api_key")
response = client.embeddings.create(
    input="LLMs are powerful tools.",
    model="text-embedding-ada-002"
)
print(response["data"][0]["embedding"])

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6. Prompt Engineering

Crafting effective prompts for better LLM responses.

prompt = """
Convert the following sentences to past tense:
1. She runs fast.
2. They eat dinner.
3. I write a book.
"""
response = client.chat.completions.create(model="gpt-4", messages=[{"role": "user", "content": prompt}])
print(response.choices[0].message["content"])

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7. LLM Deployment & API Integration

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meta-llama/Llama-2-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Explain quantum computing."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

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8. Ethical Considerations & Biases

LLMs inherit biases from training data. Example of OpenAI's moderation API:

response = client.moderations.create(input="Some sensitive content")
print(response["results"][0]["flagged"])  # Returns True if flagged

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Conclusion

Understanding LLMs, RAG, Fine-Tuning, Agents, and Deployment is crucial for AI engineers. These concepts help build powerful AI-driven applications.