Building Applications with GPT-4

gpt4_improvements:
  reasoning:
    - More accurate logical reasoning
    - Better mathematical problem-solving
    - Improved code understanding
    - Reduced hallucination (though not eliminated)

  context:
    - Longer context window (up to 32K tokens rumored)
    - Better use of provided context
    - Improved instruction following

  multimodal:
    - Image understanding
    - Visual reasoning
    - Diagram interpretation

  safety:
    - Better refusal of harmful requests
    - More aligned outputs
    - Reduced bias (but not zero)

gpt4_tradeoffs:
  cost:
    - Significantly more expensive than GPT-3.5
    - Token costs higher for input and output
    - May need tiered model approach

  latency:
    - Larger model means slower inference
    - Trade-off with quality
    - Streaming becomes more important

  availability:
    - Rate limits initially restrictive
    - Waitlists likely
    - Plan for fallback models

class ModelRouter:
    """Route requests to appropriate model based on complexity."""

    def __init__(self):
        self.models = {
            'simple': 'gpt-3.5-turbo',
            'complex': 'gpt-4',
            'vision': 'gpt-4-vision'  # Hypothetical
        }

    def route(self, request):
        complexity = self.assess_complexity(request)

        if request.has_images:
            return self.models['vision']

        if complexity > 0.7:
            return self.models['complex']

        return self.models['simple']

    def assess_complexity(self, request):
        """Heuristics for task complexity."""
        indicators = [
            request.requires_reasoning,
            request.involves_code,
            request.needs_precision,
            len(request.context) > 4000,
        ]
        return sum(indicators) / len(indicators)

cost_optimization:
  tiered_approach:
    tier_1_simple:
      model: gpt-3.5-turbo
      use_for: Classification, simple chat, summarization
      cost: $0.002/1K tokens

    tier_2_complex:
      model: gpt-4
      use_for: Complex reasoning, code generation, analysis
      cost: ~$0.03/1K tokens (estimated)

  strategies:
    pre_filter:
      - Use 3.5 to check if 4 is needed
      - Route only complex queries to GPT-4

    caching:
      - Cache more aggressively for expensive models
      - Semantic caching for similar queries

    prompt_optimization:
      - Shorter prompts for GPT-4 (it understands more with less)
      - More verbose for 3.5 if needed

class ContextManager:
    """Manage context for different model context windows."""

    def __init__(self):
        self.limits = {
            'gpt-3.5-turbo': 4096,
            'gpt-3.5-turbo-16k': 16384,
            'gpt-4': 8192,
            'gpt-4-32k': 32768,
        }

    def prepare_context(self, documents, query, model):
        limit = self.limits[model]
        available = limit - self.estimate_tokens(query) - 500  # Buffer for response

        if self.estimate_tokens(documents) <= available:
            return documents

        # Need to select/summarize
        return self.select_relevant(documents, query, available)

    def select_relevant(self, documents, query, token_limit):
        """Select most relevant documents that fit in context."""
        # Use embeddings to rank by relevance
        ranked = self.rank_by_relevance(documents, query)

        selected = []
        tokens = 0
        for doc in ranked:
            doc_tokens = self.estimate_tokens(doc)
            if tokens + doc_tokens > token_limit:
                break
            selected.append(doc)
            tokens += doc_tokens

        return selected

# Preparing for multimodal capabilities
class MultimodalProcessor:
    def process(self, inputs):
        """Handle text and image inputs."""
        text_parts = []
        image_parts = []

        for input in inputs:
            if input.type == 'text':
                text_parts.append(input.content)
            elif input.type == 'image':
                image_parts.append(self.prepare_image(input.content))

        return {
            'text': '\n'.join(text_parts),
            'images': image_parts
        }

    def prepare_image(self, image):
        """Prepare image for API (resize, encode)."""
        # Resize to reasonable dimensions
        resized = image.resize((1024, 1024))
        # Encode as base64
        return base64.b64encode(resized.tobytes())

code_capabilities:
  current_gpt35:
    - Basic code generation
    - Simple debugging
    - Syntax understanding

  expected_gpt4:
    - Complex algorithm implementation
    - Multi-file understanding
    - Architecture reasoning
    - Subtle bug detection

  application_ideas:
    - Automated code review with deeper analysis
    - Architecture documentation generation
    - Test generation with edge case detection
    - Refactoring suggestions with trade-off analysis

migration_approach:
  phase_1_testing:
    - Internal testing with GPT-4
    - Compare quality vs GPT-3.5
    - Measure latency and costs

  phase_2_shadow:
    - Run GPT-4 in shadow mode
    - Log outputs without serving
    - Analyze quality differences

  phase_3_percentage:
    - Route 10% of traffic to GPT-4
    - Monitor costs and quality
    - Adjust routing rules

  phase_4_selective:
    - Route high-value queries to GPT-4
    - Keep simple queries on GPT-3.5
    - Optimize based on data

class ModelExperiment:
    def __init__(self, experiment_config):
        self.config = experiment_config
        self.metrics = MetricsCollector()

    def run(self, request):
        variant = self.assign_variant(request)
        model = self.config.models[variant]

        start = time.time()
        response = model.generate(request)
        latency = time.time() - start

        self.metrics.record({
            'variant': variant,
            'latency': latency,
            'tokens': response.usage.total_tokens,
            'request_id': request.id
        })

        # Queue for quality evaluation
        self.queue_for_evaluation(request, response, variant)

        return response

# Abstract LLM interface for easy model swapping
from abc import ABC, abstractmethod

class LLMProvider(ABC):
    @abstractmethod
    def generate(self, prompt: str, **kwargs) -> str:
        pass

    @abstractmethod
    def estimate_cost(self, prompt: str, max_tokens: int) -> float:
        pass

class OpenAIProvider(LLMProvider):
    def __init__(self, model: str):
        self.model = model
        self.pricing = {
            'gpt-3.5-turbo': {'input': 0.0015, 'output': 0.002},
            'gpt-4': {'input': 0.03, 'output': 0.06},
        }

    def generate(self, prompt: str, **kwargs) -> str:
        response = openai.ChatCompletion.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            **kwargs
        )
        return response.choices[0].message.content

    def estimate_cost(self, prompt: str, max_tokens: int) -> float:
        input_tokens = len(prompt) / 4  # Rough estimate
        pricing = self.pricing.get(self.model, self.pricing['gpt-3.5-turbo'])
        return (input_tokens * pricing['input'] + max_tokens * pricing['output']) / 1000