This opening slide establishes a critical reality of LM engineering.
Building production grade systems requires far more than grade prompts, as highlighted on page one.
Cost management becomes one of the defining challenges as systems scale from prototypes to real world
deployments.
Early experiments often prioritize speed and capability, and that is acceptable during exploration.
However, once usage grows, even small inefficiencies compound into significant operational expenses.
Token usage, request frequency, and model selection.
All scale nonlinearly, meaning costs can rise far faster than expected.
The visual of server infrastructure reinforces this message.
LM systems are always on continuously processing requests, and every decision made at the architectural
level directly impacts recurring costs.
This section introduces battle tested strategies to optimize spending without sacrificing quality or
performance.
By the end of this section, you should understand that cost optimization is not a one time activity
or a finance concern.
It is an engineering discipline that must be designed into the system from day one.
This slide explains why cost optimization must be treated as a foundational concern, as shown on page
two.
LM costs scale rapidly across three dimensions token volume, request frequency, and model choice.
What works smoothly in a prototype with a few hundred users becomes unsustainable when traffic increases
by orders of magnitude.
The slide highlights a typical growth pattern.
Systems often experience a tenfold cost increase when moving from prototype to production without intentional
controls.
That growth can spiral out of control.
Early stage prototypes may reasonably trade efficiency for speed, but production systems require a
fundamentally different mindset.
The statement at the bottom of the slide is decisive.
If you can't control cost, you can't scale.
Every optimization decision made today compounds as usage grows.
This framing positions cost optimization not as an afterthought, but as a prerequisite for long term
success.
This slide breaks down the primary drivers of LM cost input tokens include every character in system
instructions, conversation history, and retrieve documents.
These costs apply on every request.
Output tokens are typically 2 to 10 times more expensive than input tokens, meaning verbose responses
directly impact your bill.
Request volume multiplies all other costs.
Even small inefficiencies become expensive at scale.
Techniques like batching and caching can dramatically reduce unnecessary calls.
Model tier pricing adds another dimension.
Frontier models cost significantly more than mid-tier or specialized models.
The reality check at the bottom of the slide is especially important.
A seemingly small overhead, such as an extra hundred tokens per request, becomes massive at scale
on 1 million requests.
That translates to 100 million unnecessary tokens and a substantial monthly expense.
This slide reinforces the need for ruthless efficiency.
This slide emphasizes that tokens are the fundamental unit of LM cost.
Every token sent or received costs money, and that cost recurs with every request token.
Optimization is not about trimming words.
It is about architectural efficiency.
The slide outlines concrete ways to reduce token usage.
Writing concise system instructions.
Pruning conversation history to essential context, removing redundant information, and using structured
formats instead of verbose natural language.
Each of these changes reduces recurring costs without degrading quality.
Equally important are the mistakes to avoid repeating instructions in every user message, including
full transcripts when summaries suffice, and requiring verbose formatting that could be handled in
post-processing.
All waste tokens.
The golden rule at the bottom of the slide is worth repeating.
Every unnecessary token is a recurring cost that compounds with every request.
Effective teams audit prompts as aggressively as they audit code.
This slide introduces compression as a precision engineering practice.
The goal is not to cut corners, but to send only what the model truly needs.
The first strategy is summarizing long histories using a smaller, cheaper model to create summaries
can reduce input tokens by 70 to 90% while preserving meaning.
The second strategy is structuring prompts using formats like JSON or templates, which reduce natural
language overhead and improve consistency.
Removing chain of thought from user facing prompts is another major optimization.
While verbose reasoning is useful during development, production systems should request direct answers
unless reasoning is essential.
Finally, encoding static instructions once in the system role instead of repeating them in every request
can immediately cut costs by 20 to 40%.
The best practice note at the bottom reframes prompts as API payloads.
Every sentence, example, and piece of context must justify its existence.
This slide explains why caching is one of the most powerful cost reduction strategies.
Many LM requests are repetitive, making caching and obvious optimization.
The slide lists what to cache aggressively.
Embeddings are expensive to generate but rarely change, making them ideal for indefinite caching.
Repeated prompts such as FAQs often achieve cache hit rates of 40 to 60% in production systems.
Deterministic completions, especially when temperature is set to zero, are predictable and safe to
cache.
Tool results from external APIs are also excellent candidates.
Caching infrastructure operates at multiple layers.
In-memory caches like Reddis for submillisecond access, application level caches for medium frequency
queries, and CDN or edge caches for global distribution.
The key insight is clear most LLM traffic follows the 80 over 20 rule.
Effective caching can reduce API costs by 40 to 70%.
This slide addresses the risks associated with caching while caching saves money.
Improper implementation can serve stale or incorrect responses.
The slide identifies three primary risks.
Stale answers occur when underlying data changes.
Incorrect reuse happens when similar but distinct queries match the same cache entry.
Context mismatch arises when user specific information leaks across requests.
Mitigation strategies are outlined clearly.
Parameterized cache keys should include user ID, session, model, version, and temperature.
Time to live values must reflect content volatility.
Selective caching avoids high risk or personalized outputs.
Cache versioning ensures changes in prompts or models invalidate old entries automatically.
The caching rule at the bottom summarizes the philosophy cache aggressively but intentionally.
Every caching decision is a trade off between cost savings and accuracy.
This slide explains why model selection is a major cost lever.
Not every request requires the most powerful and expensive model.
Strategic matching of model capability to task complexity can reduce costs by 60 to 80%.
The slide breaks down selection criteria, cost per thousand tokens, latency requirements, task capability,
and context window size.
Smaller models are faster and cheaper, making them ideal for real time interactions and simple tasks.
Larger models should be reserved for complex reasoning, planning, and agentic workflows.
The slide provides practical assignment patterns.
Use small models for classification, routing, extraction, summarization, and formatting.
Reserve large models for strategic planning, complex reasoning chains and creative generation.
The core principle is clear task complexity, not habit, should drive model selection.
The final slide introduces dynamic model routing, the most advanced cost optimization strategy discussed
in this section.
Instead of using a single model, production systems analyze each request in real time and routed to
the most appropriate model.
The process involves classifying requests by complexity and user tier, routing them strategically validating
output quality, and escalating to more capable models only when necessary.
Three routing strategies are highlighted.
Complexity based routing uses a lightweight classifier to determine task difficulty.
Confidence based escalation retries with a stronger model only if quality checks fail.
User tier optimization delivers premium experiences where appropriate while controlling costs for free
tiers.
The result is enterprise grade cost control that balances quality, cost and latency dynamically.
The final message reinforces a central theme of this section.
The best systems optimize continuously, not just once.