Token Efficiency in LLM Use, Agentic AI, and Beyond
Course Curriculum
20 modules designed to master the subject.
Module 1: Understanding Tokens and Cost
Learn how tokens work, how they are priced, and the impact of input vs output processing.
What Tokens Are and How They Are Counted
Discover the fundamental building blocks of LLM communication. Learn how text is transformed into tokens, why character counts don't equal token counts, and how to master the tokenization process.
The Economics of Tokens: Input vs. Output Processing
Master the economic divide in LLM usage. Learn why output tokens cost more, how attention mechanisms process them differently, and how to optimize your architecture for maximum ROI.
Understanding Context Window Limits: Hitting the Wall
Master the constraints of LLM memory. Learn how context windows work, why 'infinite' windows are a myth, and how to manage large-scale data without overwhelming your model.
Token Pricing Models: Navigating the Cloud Economy
Decode the financial structures of the AI industry. Learn the difference between on-demand, provisioned, and batched pricing, and discover how to arbitrage between providers for maximum efficiency.
Latency and Throughput: The Speed of Tokens
Master the temporal dynamics of LLMs. Learn why token generation speed varies, how to measure Time to First Token (TTFT), and why high throughput often comes at the cost of high latency.
Module 2: Where Token Waste Comes From
Identify common sources of token bloat, from verbose prompts to repeated system instructions.
The Cost of Repetition: Optimizing System Prompts
Stop paying for the same pixels twice. Learn how repeated system prompts drain your token budget, how to implement 'Instruction Isolation', and why 'Prompt Engineering' is often just cleaning up clutter.
The Verbosity Trap: Cutting Linguistic Fluff
Master the art of 'High-Density' prompting. Learn why polite greetings waste money, how to use 'Token-First' syntax, and how to command your LLM with surgical precision.
Redundant Context Injection: The RAG Token Drain
Stop flooding your LLM with duplicate data. Learn why naive RAG architectures waste millions of tokens, how to deduplicate context, and why 'Cross-Chunk Redundancy' is the enemy of efficiency.
Large, Unfiltered Documents: The Cost of Lazy Ingestion
Master the art of 'Context Grooming'. Learn why raw document dumping is a financial disaster, how to strip metadata noise, and how to use 'Selective Ingestion' to keep your vector index lean and clean.
Uncontrolled Agent Loops: The Token Fire
Protect your budget from autonomous runaway. Learn why agents get stuck in infinite 'Thought' loops, how to implement circuit breakers, and how to govern agent reasoning before it drains your wallet.
Module 3: Token Efficiency as a Design Principle
Design AI systems with a focus on minimal context and high information density.
Shift-Left Token Management: Designing for Scale
Learn why token efficiency must start at the white-board, not the debugger. Explore the 'Shift-Left' philosophy for AI architecture and how to build cost-first systems.
Information Density vs. Word Count: The Signal Ratio
Learn to maximize the 'Intelligence per Token' in your applications. Master the techniques of semantic compression, keyword mapping, and structural density.
Choosing the Right Architecture: RAG vs. Fine-tuning vs. Context
Master the fundamental trade-offs of AI memory. Learn when to use the context window, when to index into a vector database, and when to bake knowledge into model weights.
The 'Thin Context' Workflow: Tactical Precision
Learn the step-by-step workflow for implementing high-efficiency RAG. Master chunking, filtering, and re-ranking to deliver the perfect context to your model.
Benchmarking and ROI for Efficiency: The Bottom Line
Learn to quantify the value of token optimization. Master the metrics of ROI, cost-per-successful-query, and efficiency-aware benchmarking.
Module 4: Prompt Engineering for Token Efficiency
Master compact instruction writing and output length control techniques.
Writing Compact Instructions: The Art of the 'Micro-Prompt'
Master the grammatical and structural techniques for token-dense instructions. Learn to replace paragraphs with properties and sentences with symbols.
Output Length Control: Stopping the Token Leak
Master the techniques for limiting model verbosity. Learn the difference between token limits and semantic limits, and how to enforce budget-friendly generation.
Avoiding Recursive System Prompts: The State Trap
Master the art of 'Context Handover'. Learn how to prevent system prompt duplication in multi-agent and multi-turn systems.
Token-Efficient Formatting: Markdown vs. XML vs. JSON
Master the structural semantics of tokens. Learn which format is the most compact for data injection and how to avoid the 'Syntax Tax'.
Preamble and Postamble Suppression: Cutting the 'Chatter'
Learn how to eliminate conversational padding from AI responses. Stop the model from saying 'Sure, I can help' and save thousands of output tokens.
Module 5: Prompt Caching and Reuse
Leverage modern model caching capabilities to reduce latency and recurring costs.
How Prompt Caching Works: The New ROI Layer
Discover the most powerful cost-saving feature in modern LLMs. Learn how prompt caching reduces latency and slashes repeating token costs by up to 90%.
Claude Prompt Caching: Slashing Costs by 90%
Master the implementation of Anthropic's prompt caching. Learn how to use 'Cache Breakpoints', manage cost tiers, and build high-performance Claude applications.
AWS Bedrock Prompt Caching: Enterprise Efficiency
Learn how to implement prompt caching within the AWS Bedrock infrastructure. Master the differences between native model caching and Bedrock's 'Context Caching' features.
Managing Cache Lifecycles: Keeping the Cache Hot
Master the temporal dynamics of prompt caching. Learn how to keep your caches 'warm', when to allow them to expire, and how to optimize for 'Churn' in your user base.
Architectural Design for Caching-First Apps: Thinking in Blocks
Re-envision your AI backend for the caching era. Learn how to structure prompts as immutable layers, manage dynamic state, and build 'Caching-Native' applications.
Module 6: Context Management and Window Optimization
Optimize context windows using sliding windows, summarization, and selective memory.
Context Management: Sliding Windows vs. Summary Windows
Learn the two primary strategies for managing long conversations. Master the art of 'Context Truncation' and 'Semantic Compression' to keep your agent's memory lean and focused.
Selection and Pruning: Smart Memory Deletion
Learn the advanced techniques for 'Intelligent Forgetting'. Discover how to use semantic search to retrieve history instead of sending it all, and how to prune the useless parts of a conversation.
Knowledge Compression: Zipping Information for LLMs
Master the art of 'Semantic Zipping'. Learn how to turn 1,000 words of context into 100 tokens of high-density facts using multi-model pipelines.
Topic-Based Context Isolation: Segregating the Brain
Master the architecture of 'Thread Isolation'. Learn how to prevent context contamination, separate unrelated user streams, and optimize tokens by only sending what is logically relevant.
Multi-Turn Management in Agents: State vs. Memory
Master the complexities of long-running autonomous tasks. Learn how to manage 'Plan Drift', optimize agent state, and prevent 'Loop Exhaustion' through efficient token turns.
Module 7: Retrieval-Augmented Generation (RAG) Efficiency
Build efficient RAG systems by tuning chunk sizes and retrieval strategies.
Tuning Chunk Sizes: The Foundation of RAG Efficiency
Master the mathematical balance of retrieval. Learn why 'Chunk Size' is a financial variable, how to avoid 'Fragmented Reasoning', and how to calculate the perfect token-to-data ratio.
Hybrid Search: The Efficiency of Keywords
Learn how to combine Vector embeddings and BM25 keywords for maximum token ROI. Discover why 'Keyword First' retrieval can reduce LLM reasoning costs by 50%.
The Role of Re-rankers in Token Savings: Precision RAG
Master the most powerful tool for RAG cost reduction. Learn why re-rankers pay for themselves in seconds, and how to build a 'Recall to Precision' funnel.
Context Injection Patterns: Formatting for Attention
Learn the high-density patterns for injecting RAG results into LLMs. Master the use of XML tags, JSON arrays, and 'Citation-First' prompting.
Evaluation of RAG ROI: Measuring Search Success
Master the metrics of RAG efficiency. Learn to calculate the value of high-precision retrieval and how to build a business case for token optimization.
Module 8: Embeddings and Retrieval Cost Control
Manage costs associated with embedding generation and index updates.
Managing Embedding Costs: The Hidden Infrastructure Bill
Learn to optimize the cost of turning text into vectors. Master the economics of embedding models and discover how to reduce 'Re-Indexing' waste.
Optimizing Index Updates: The Delta Strategy
Master the lifecycle of a vector database. Learn how to manage 'Stale' vectors, handle schema migrations without re-calculating embeddings, and minimize ingestion costs.
Choosing Cost-Effective Vector Stores: Storage Economics
Master the financial trade-offs of vector databases. Learn the difference between Managed vs. Self-hosted, and how to scale your vector index without scaling your bill.
Dimensionality Reduction: Compressing the Vector Space
Learn how to reduce vector size without loss of accuracy. Master PCA, Matryoshka embeddings, and the mathematics of high-density search.
Local vs. Cloud Embeddings: Breaking the API Tether
Master the infrastructure of embeddings. Learn when to host your own embedding models and how to leverage 'Local Performance' for zero-cost RAG ingestion.
Module 9: Agentic AI and Token Explosion
Understand why agent reasoning loops consume excessive tokens and how to control them.
Agentic AI: The Token Explosion
Discover why autonomous agents consume 10x more tokens than standard RAG. Learn to identify 'Recursive Thought' and how to tame the budget-breaking agent.
Multi-Agent Efficiency: The Power of Specialization
Learn how to reduce token waste by splitting large agents into many small, 'narrow' agents. Master the 'Supervisor Pattern' and 'Handoff' logic for cost-effective AI.
Tool Call Optimization: Reducing the 'Syntax Tax'
Learn how to minimize the cost of tool definitions and usage. Master 'Schema Pruning', manual tool calling, and compressed response formats.
The 'Planning' Step: Cost vs. Performance
Learn to manage the heavy reasoning turns in agentic AI. Master 'Static Planning' vs. 'Dynamic Planning' and how to budget for agentic intelligence.
Agent Throttling and Budgeting: The Final Frontier
Protect your infrastructure from recursive agent debt. Learn to implement token-based circuit breakers, rate limits, and budget-aware agent governors.
Module 10: Designing Token-Efficient Agents
Implement explicit termination rules and bounded planning depth for agents.
Explicit Termination Rules: Stopping the Agent
Learn how to define 'Success and Failure' for autonomous agents. Master the implementation of termination nodes to prevent infinite reasoning loops.
Bounded Planning Depth: Complexity Control
Learn how to limit the foresight of autonomous agents. Master the art of 'Incremental Execution' to prevent expensive, over-engineered agent plans.
Reasoning Conciseness: Sharpening the Agent's Thought
Learn how to prune the 'Internal Monologue' of autonomous agents. Master the 'Technical Shorthand' for agentic reasoning and reduce output costs by 70%.
Action Verification: Precision over Persistence
Learn how to reduce agentic retry-loops through early verification. Master the 'Self-Correction' techniques that save thousands of tokens on failed attempts.
Human-in-the-Loop: The Ultimate Token Filter
Learn how to use human intervention as a cost-control strategy. Master the architectural patterns for 'Interruption' and 'Guidance' in agentic AI.
Module 11: Memory vs Cache vs State
Manage agent state and memory without causing catastrophic context bloat.
The Agentic Storage Hierarchy: Memory vs. Cache vs. State
Master the three pillars of AI data management. Learn when to use the context window, when to use the GPU cache, and when to offload to a database.
Ephemeral vs. Permanent State: Managing Persistence
Learn to distinguish between temporary 'Reasoning State' and permanent 'Fact State'. Master the patterns for offloading agent data to SQL for token savings.
History Serialization: Compact Memory Formats
Learn how to turn long chat histories into ultra-compact byte-strings. Master the art of 'Semantic Minification' for token-efficient history.
Long-Term Memory: Scaling with External Databases
Learn how to build 'Infinity-Scale' memory. Master the integration of Postgres, Redis, and Graph databases into your agentic token workflow.
Managing Reasoning Logs: Externalizing the 'Why'
Learn how to store agentic thoughts without bloating the context window. Master the separation of 'Execution Logs' and 'Reasoning Logs' for enterprise AI.
Module 12: Multi-Agent Systems and Token Control
Optimize communication and shared context between multiple agents.
Multi-Agent Orchestration: Controlling the Fleet
Learn how to manage multiple agents without context explosion. Master the synchronization patterns that keep token costs in check.
The Advanced Supervisor: Cost-Aware Routing
Learn how to build a 'Financial Router' for multi-agent systems. Master the art of choosing models based on task complexity and remaining token budget.
Communication Protocols: The Agentic DSL
Learn how to optimize inter-agent communication. Master the 'Technical Protocol' and 'Shared State' patterns that eliminate conversational overhead.
Shared vs. Private Context: Managing the Common Ground
Learn the privacy and efficiency trade-offs of multi-agent memory. Master the art of 'Selective Knowledge Sharing' to prevent agent confusion and token bloat.
Cost Attribution: Who Spent the Budget?
Learn to track and attribute token costs in complex agent networks. Master the metrics of 'Cost per Task' and 'Agent ROI'.
Module 13: Structured Outputs and Token Savings
Use schemas to eliminate conversational fluff and ensure machine-readable data.
Structured Outputs: Eliminating the Fluff
Learn why structured data (JSON/YAML) is a financial optimization tool. Master the art of forcing machine-readable answers to save on token overhead.
JSON vs. YAML vs. Markdown: The Token Benchmarks
Master the data-format economics of AI. Learn which format uses the fewest tokens for your specific data structure.
Enforcing Schema Constraints: The Pydantic Shield
Master the art of 'Strict Output'. Learn how to use Pydantic to enforce token-efficient formats and prevent 'Schema Hallucination'.
Handling Malformed Output: The Graceful Pivot
Learn how to handle invalid JSON/YAML without breaking the bank. Master the 'Self-Correction' techniques that save tokens on retries.
Structured Verbosity: The 'Key' to Savings
Learn how to optimize the content of your JSON/YAML fields. Master the 'Technical Shorthand' and 'Mapped Constants' patterns.
Module 14: Model Selection and Token Economics
Choose the right size and type of model for specific tasks to optimize cost.
The Intelligence vs. Cost Spectrum: Choosing the Tool
Master the economics of model selection. Learn how to map your technical tasks to the specific 'Tier' of model that maximizes ROI.
The Power of Small Models: Speed and Savings
Learn how to leverage under-10B parameter models for high-volume tasks. Master the 'Instruction Distillation' techniques for small model success.
Prompt Routing: The Traffic Controller
Master the architecture of 'Dynamic Routing'. Learn how to build a router that sends simple queries to cheap models and complex queries to experts.
Evaluating Model ROI: The Intelligence/Price Audit
Learn how to quantify the value of your model selection. Master the metrics for 'Capability per Dollar' and build a performance-based leaderboard.
Future-Proofing: Preparing for Negative Token Prices
Learn how to architect for the long term. Master the strategies for a world where tokens are 'Too Cheap to Meter' and focus shifts to Latency and Logic.
Module 15: Inference Optimization Beyond Prompts
Fine-tune inference parameters like temperature and max tokens for efficiency.
Temperature and Top-P: The 'Repeat' Tax
Learn how inference parameters affect your token bill. Master the balance of 'Creativity vs. Conciseness' and how high temperature leads to wasted tokens.
Max Tokens vs. Stop Sequences: Hard Termination
Learn how to physically stop the model from wasting tokens. Master the difference between 'Truncation' and 'Graceful Stop'.
Frequency and Presence Penalties: Killing the Loop
Learn how to use penalty parameters to prevent word loops and repetitive descriptions. Master the 'Diversity/Brevity' balance.
Streaming vs. Batching: Delivery Economics
Master the economics of API delivery. Learn how 'Batch API' can save 50% on your token bill and when to prioritize 'Streaming' for UX efficiency.
Speculative Decoding: Small Speed, Large Intelligence
Learn how to use small models to accelerate large models. Master the architecture of 'Speculative Sampling' for ultra-fast token generation.
Module 16: Token Efficiency in Production Systems
Implement rate limiting, token budgets, and cost-aware routing at scale.
Rate Limiting and Token Quotas: Resource Governance
Learn how to protect your AI infrastructure from abuse and over-spending. Master the implementation of 'Token Buckets' and tiered user quotas.
Cost-Aware Routing at Scale: Global Optimization
Learn how to route queries across multiple providers and models to find the cheapest 'Clean' solution. Master the art of 'Provider Flipping' for token savings.
Real-Time Token Monitoring: The Pulse of AI
Learn how to build observability into your token pipelines. Master the integration of Prometheus, Grafana, and custom telemetry for cost-tracking.
Handling Burst Traffic: Scaling without Spiking
Learn how to survive 'Viral Moments' and peak loads. Master the architectural shifts from 'Synchronous' to 'Asynchronous' for token stability.
Enterprise Token Budgets: AI Governance
Learn how to manage AI costs for large organizations. Master the implementation of 'Departmental Quotas' and billable token units.
Module 17: Observability and Token Accounting
Measure and attribute token usage to specific features and users.
The Token Audit: Analyzing the Bill
Learn how to perform a deep-dive audit of your AI application. Master the techniques for identifying 'Zombie Context' and 'Instruction Rot'.
Token Lineage: Tracking the Thread
Learn how to trace the flow of tokens through complex agentic chains. Master the 'Lineage Map' for debugging cost and accuracy.
Visualizing Cost: The Grafana Command Center
Learn how to build a world-class AI dashboard. Master the visualization of token burn, latency, and model ROI for executive visibility.
Token Tags: Granular Cost Attribution
Learn how to tag and track every token back to a project, user, or department. Master the 'Metadata-Driven' billing architecture.
Predictive Token Accounting: Forecasting the Bill
Learn how to predict your future AI costs. Master the math of 'Token-per-DAU' and linear regression for budget planning.
Module 18: Security, Privacy, and Token Hygiene
Protect sensitive data while minimizing exposed context in agents.
Prompt Injection vs. Token Burn: The Hidden Cost
Learn how malicious prompt injections can bankrupt your AI budget. Master the defense strategies that keep your system safe and efficient.
Defensive Prompting: Safety with Brevity
Learn how to protect your agent without bloating your system prompt. Master the 'Concise Guardrail' patterns for token efficiency.
Input Sanitization: Pre-Token Cleaning
Learn how to strip noise from user inputs before they hit the LLM. Master the techniques for cleaning HTML, Markdown, and redundant whitespace.
Recursive Attacks: Stopping the Infinite Loop
Learn how to defend against agentic recursive attacks. Master the 'Circuit Breaker' and 'Depth Limiter' patterns for token safety.
Privacy and Compression: Secret Savings
Learn how to redact PII and sensitive data while reducing token counts. Master the 'Encryption-Lite' and 'Token Hashing' patterns.
Module 19: Token Efficiency at Scale
Architect multi-tenant systems for burst traffic and cost forecasting.
The Efficiency Profit: Quantifying the Value
Learn how to calculate the bottom-line impact of your token engineering. Master the metrics for 'Profit per Token' and 'COGS Optimization'.
Stakeholder Communication: Selling Efficiency
Learn how to present your technical token work to non-technical leaders. Master the art of the 'Business Case' for AI optimization.
Optimization vs. Accuracy: The Performance Frontier
Learn how to manage the trade-off between cost and capability. Master the 'Diminishing Returns' curve of AI engineering.
Long-Term Agent Economics: The Scale Factor
Learn how agentic AI costs scale over years, not just turns. Master the lifecycle economics of autonomous systems.
Sustainable AI Pricing: Balancing Cost and Value
Learn how to price your AI features without losing money. Master the 'Usage-Based', 'Tiered', and 'Credit-Based' pricing models.
Module 20: Capstone Project
Build a complete agentic platform with strict token budgets and caching.
Capstone: Building the 'Budget-First' Researcher
Start your final project. Architect an autonomous research agent that minimizes costs using everything you have learned in this course.
Capstone: Building the Multi-Model Router
Implement the 'Intelligence Controller' for your capstone project. Learn how to toggle between models based on task depth and remaining budget.
Capstone: Implementing Persistent Memory
Build the memory layer for your capstone project. Learn how to use external storage to keep your agent's context 'Thin' and efficient.
Capstone: The Final Efficiency Pass
Fine-tune your researcher for maximum ROI. Master the final tweaks that shave off the remaining pennies to hit your $0.10 goal.
Final Review: The Future of Efficient AI
Review the core principles of the course and prepare for the next phase of your AI career. Master the 'Efficiency Mindset' for lifelong learning.
Course Overview
Format
Self-paced reading
Duration
Approx 6-8 hours
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