OpenAI DevDay: What It Means for Developers

gpt4_turbo:
  context_window:
    before: 8K or 32K tokens
    after: 128K tokens
    impact: Entire codebases, long documents fit in context

  knowledge_cutoff:
    before: September 2021
    after: April 2023
    impact: More current information

  pricing:
    input: $0.01/1K tokens (3x cheaper than GPT-4)
    output: $0.03/1K tokens (2x cheaper)
    impact: Significantly reduced costs

  capabilities:
    - JSON mode (guaranteed valid JSON)
    - Seed parameter (reproducible outputs)
    - Improved function calling
    - Better instruction following

assistants_api:
  what: Stateful, tool-using agents as a service
  key_features:
    persistent_threads:
      - Conversation history managed by OpenAI
      - No manual context management
      - Messages persist across requests

    built_in_tools:
      code_interpreter:
        - Execute Python code
        - Generate visualizations
        - Process files
      retrieval:
        - Upload documents
        - Automatic RAG
        - No vector DB needed
      function_calling:
        - Call your APIs
        - Same as before, improved

  implications:
    - Lower barrier to building agents
    - Reduced infrastructure needs
    - But: less control, vendor lock-in

custom_gpts:
  what: No-code custom ChatGPT versions
  features:
    - Custom instructions
    - Knowledge files
    - Actions (API integrations)
    - Shareable/monetizable

  developer_impact:
    positive:
      - Rapid prototyping
      - Distribution channel
      - Monetization opportunity

    concerns:
      - Limited customization
      - Platform dependency
      - Competition from simple solutions

import openai

# New model with 128K context
response = openai.ChatCompletion.create(
    model="gpt-4-1106-preview",  # GPT-4 Turbo
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": long_document}  # Can now be huge
    ],
    response_format={"type": "json_object"},  # Guaranteed JSON
    seed=42  # Reproducible outputs
)

import openai

# Create an assistant
assistant = openai.beta.assistants.create(
    name="Data Analyst",
    instructions="You analyze data and create visualizations.",
    model="gpt-4-1106-preview",
    tools=[
        {"type": "code_interpreter"},
        {"type": "retrieval"}
    ]
)

# Upload files for retrieval
file = openai.files.create(
    file=open("data.csv", "rb"),
    purpose="assistants"
)

# Create a thread (conversation)
thread = openai.beta.threads.create()

# Add a message
message = openai.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Analyze the sales data and create a chart",
    file_ids=[file.id]
)

# Run the assistant
run = openai.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id
)

# Wait for completion and get messages
# (In practice, use polling or streaming)

strategic_shifts:
  rag_simplified:
    before: Build vector DB, embedding pipeline, retrieval
    after: Upload files, OpenAI handles RAG
    consideration: Trade-off control for simplicity

  agent_infrastructure:
    before: Build thread management, tool execution
    after: Assistants API handles state
    consideration: Lock-in vs. development speed

  cost_structure:
    before: GPT-4 expensive, limit usage
    after: 3x cheaper, more viable for production
    consideration: New use cases become feasible

  context_strategies:
    before: Carefully manage context, summarize
    after: 128K tokens, often fits everything
    consideration: Simpler architectures possible

build_vs_buy_reevaluation:
  use_assistants_api_when:
    - Rapid prototyping
    - Simple use cases
    - Limited engineering resources
    - Acceptable vendor lock-in

  build_your_own_when:
    - Need fine-grained control
    - Specific retrieval requirements
    - Multi-model strategies
    - Data sovereignty requirements
    - Custom evaluation needs

  hybrid_approach:
    - Use Assistants for simple features
    - Custom for core differentiators
    - Evaluate as capabilities evolve

existing_app_actions:
  immediate:
    - Test GPT-4 Turbo compatibility
    - Evaluate cost savings
    - Try JSON mode for structured outputs

  short_term:
    - Consider context window expansion opportunities
    - Evaluate Assistants API for appropriate features
    - Update rate limit handling for new models

  evaluate:
    - RAG system: keep custom or migrate?
    - Agent infrastructure: rebuild on Assistants?
    - Cost/benefit of migration

new_project_decisions:
  start_with:
    - GPT-4 Turbo as default
    - JSON mode for structured output
    - Assistants API for prototyping

  consider:
    - Is 128K context enough? (Usually yes)
    - Do we need custom retrieval? (Often no)
    - Can we accept OpenAI dependencies?

  architecture:
    - Simpler is now often better
    - Built-in features before custom
    - Abstract for future flexibility