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Prerequisites
Before starting this chapter, you should have:
- Completed "LLM Foundations for Agent Builders" course or equivalent knowledge
- Basic understanding of REST APIs and HTTP request/response patterns
- Familiarity with Python async/await patterns
- Understanding of JSON data formats and schema validation
- Basic knowledge of authentication mechanisms (API keys, tokens)
Goals
By the end of this chapter, you will be able to:
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- Compare capabilities acrossmajor LLM providers (Anthropic, OpenAI, Google) - understanding their unique strengths, pricing models, and optimal use cases for production deployments
- This skill is fundamental for Python development and agent building.
- You will practice this through hands-on exercises in the lab.
- Understanding this concept enables building more sophisticated agent applications.
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- Implement streaming andbatch request patterns effectively - choosing the right pattern for user-facing applications versus background processing workloads
- This skill is fundamental for Python development and agent building.
- You will practice this through hands-on exercises in the lab.
- Understanding this concept enables building more sophisticated agent applications.
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Token Economics and Cost Optimization
- Calculate and optimizetoken costs across different pricing models - building accurate cost estimation and tracking systems from day one
- This skill is fundamental for Python development and agent building.
- You will practice this through hands-on exercises in the lab.
- Understanding this concept enables building more sophisticated agent applications.
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- Handle rate limitsand quotas appropriately for each provider - implementing proactive monitoring and graceful degradation strategies
- This skill is fundamental for Python development and agent building.
- You will practice this through hands-on exercises in the lab.
- Understanding this concept enables building more sophisticated agent applications.
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- Implement secure apikey management and authentication patterns - following security best practices for production environments
- This skill is fundamental for Python development and agent building.
- You will practice this through hands-on exercises in the lab.
- Understanding this concept enables building more sophisticated agent applications.
Key Terminology
Token
The fundamental unit of text processing in LLMs. Tokens are subword units that models use to process text. On average, one token equals approximately 4 characters in English text or about 0.75 words.
Context Window
The maximum number of tokens a model can process in a single request, including both input (prompt) and output (completion). Larger context windows allow processing of longer documents but may increase latency and cost.
Input Tokens
Tokens sent to the model as part of the prompt, including system instructions, user messages, and any context provided. These are typically priced lower than output tokens.
Output Tokens
Tokens generated by the model as the response. These are typically 3-15x more expensive than input tokens because they require more computational resources to generate.
Cached Tokens
Previously processed input tokens that can be reused in subsequent requests at a significant discount (50-90% depending on provider).
Rate Limit
The maximum number of requests or tokens a provider allows within a specific time window. Exceeding rate limits results in HTTP 429 errors.
RPM (Requests Per Minute)
The maximum number of API calls allowed per minute.
TPM (Tokens Per Minute)
The maximum number of tokens that can be processed per minute, combining both input and output tokens.
Streaming
A response delivery pattern where tokens are sent incrementally as they are generated, reducing perceived latency for end users.
Batch API
An asynchronous processing mode where multiple requests are submitted together for non-urgent processing at reduced cost.
Prompt Caching
A cost-optimization feature that stores and reuses frequently repeated portions of prompts to reduce token costs.