GPT-4

Cost & access

GPT-4 is proprietary via OpenAI. Typical billing: input + output tokens; ChatGPT-style subscriptions are separate from API access. With a 8K–128K tokens context window, long PDFs or chat histories increase input tokens quickly—trim history or summarize older turns in production.

Functional understanding

• Foundational GPT-4 release that defined modern chat LLMs.
• Modalities: text · License: Proprietary · Released 2023-03-14.
• Best-fit workflows for this model:
• • Drafting, summarization, and structured extraction from long documents.

Technical foundation

• OpenAI reports Undisclosed parameters; training data: Undisclosed.
• Context: 8K–128K tokens. Open weights: no.
• GPT-4 is positioned as a general-purpose model in the OpenAI lineup.

First API call

Set OPENAI_API_KEY and call GPT-4 via the Responses or Chat Completions API.

from openai import OpenAI

client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello from GPT-4"}],
)
print(resp.choices[0].message.content)

Important technical topics

• Prompting GPT-4: be explicit about output format. Weak: "Analyze this." Better: "Return JSON with fields id, total, date for OpenAI billing data."
• Temperature: use 0–0.3 for extraction and compliance on GPT-4; 0.7–1.0 for brainstorming.
• Tokens: GPT-4 bills by tokens (~¾ word each). Undisclosed parameters affect capability; your bill is driven by context length and call volume.
• Context window (8K–128K tokens): everything in one request—system prompt, tools, RAG chunks, and history—must fit. Truncate or summarize when approaching the limit for GPT-4.

Real enterprise patterns

• RAG with GPT-4: retrieve from your vector DB, cite sources in the prompt.
• Tool calling: define JSON schemas; let GPT-4 request functions, not free-form SQL.
• Eval suite: regression prompts before each model or prompt change.
• Cost routing: default to GPT-4 for hard tasks; smaller sibling model for triage.

Production & security

• Secrets: never commit keys for GPT-4; use vault + per-environment rotation.
• PII: mask before inference; log redacted prompts only.
• Observability: trace id per request; log model=gpt-4, tokens in/out, latency.
• Rate limits: handle OpenAI 429/5xx with exponential backoff and circuit breakers.
• Guardrails: schema-validate JSON; block disallowed topics; cross-check numbers against source docs.

Mini projects with this model

• Support copilot: GPT-4 drafts replies from KB snippets.
• Contract clause extractor with human approval.
• Weekly metrics narrative from SQL + CSV exports.
• Agent that files expenses from receipt photos (if multimodal).

Suggested stack

• Language: Python 3.11+
• LLM: GPT-4 through openai Python SDK
• Optional: OpenAI Agents SDK for tools
• UI: Streamlit or Next.js for internal tools
• APIs: FastAPI
• Vector DB (RAG): Pinecone / Chroma / pgvector

Learning path

• Python basics
• HTTP/REST and environment variables
• OpenAI authentication and GPT-4 model id (gpt-4)
• First successful call to GPT-4
• Prompt design and JSON / structured outputs
• RAG
• Tool use / function calling
• Evals and regression sets
• Production deploy + monitoring