WEBVTT 00:00.160 --> 00:04.880 This opening slide establishes a critical reality of LM engineering. 00:05.240 --> 00:11.800 Building production grade systems requires far more than grade prompts, as highlighted on page one. 00:12.200 --> 00:18.520 Cost management becomes one of the defining challenges as systems scale from prototypes to real world 00:18.520 --> 00:19.400 deployments. 00:19.960 --> 00:26.360 Early experiments often prioritize speed and capability, and that is acceptable during exploration. 00:26.880 --> 00:33.760 However, once usage grows, even small inefficiencies compound into significant operational expenses. 00:34.320 --> 00:37.880 Token usage, request frequency, and model selection. 00:37.880 --> 00:42.880 All scale nonlinearly, meaning costs can rise far faster than expected. 00:43.480 --> 00:47.120 The visual of server infrastructure reinforces this message. 00:47.720 --> 00:54.640 LM systems are always on continuously processing requests, and every decision made at the architectural 00:54.640 --> 00:57.520 level directly impacts recurring costs. 00:58.080 --> 01:04.830 This section introduces battle tested strategies to optimize spending without sacrificing quality or 01:04.830 --> 01:05.630 performance. 01:07.110 --> 01:13.830 By the end of this section, you should understand that cost optimization is not a one time activity 01:13.990 --> 01:15.670 or a finance concern. 01:16.190 --> 01:20.710 It is an engineering discipline that must be designed into the system from day one. 01:21.030 --> 01:28.310 This slide explains why cost optimization must be treated as a foundational concern, as shown on page 01:28.310 --> 01:28.750 two. 01:29.230 --> 01:36.830 LM costs scale rapidly across three dimensions token volume, request frequency, and model choice. 01:37.350 --> 01:43.710 What works smoothly in a prototype with a few hundred users becomes unsustainable when traffic increases 01:43.710 --> 01:45.270 by orders of magnitude. 01:45.910 --> 01:48.510 The slide highlights a typical growth pattern. 01:48.910 --> 01:56.030 Systems often experience a tenfold cost increase when moving from prototype to production without intentional 01:56.030 --> 01:56.790 controls. 01:57.110 --> 01:59.550 That growth can spiral out of control. 02:00.150 --> 02:06.350 Early stage prototypes may reasonably trade efficiency for speed, but production systems require a 02:06.350 --> 02:08.150 fundamentally different mindset. 02:08.710 --> 02:11.510 The statement at the bottom of the slide is decisive. 02:11.790 --> 02:14.710 If you can't control cost, you can't scale. 02:15.150 --> 02:19.630 Every optimization decision made today compounds as usage grows. 02:20.230 --> 02:26.470 This framing positions cost optimization not as an afterthought, but as a prerequisite for long term 02:26.470 --> 02:27.150 success. 02:27.590 --> 02:34.910 This slide breaks down the primary drivers of LM cost input tokens include every character in system 02:34.910 --> 02:38.710 instructions, conversation history, and retrieve documents. 02:39.270 --> 02:41.710 These costs apply on every request. 02:42.230 --> 02:48.550 Output tokens are typically 2 to 10 times more expensive than input tokens, meaning verbose responses 02:48.550 --> 02:50.230 directly impact your bill. 02:50.670 --> 02:53.750 Request volume multiplies all other costs. 02:54.190 --> 02:57.630 Even small inefficiencies become expensive at scale. 02:58.150 --> 03:03.230 Techniques like batching and caching can dramatically reduce unnecessary calls. 03:03.710 --> 03:06.190 Model tier pricing adds another dimension. 03:06.780 --> 03:11.420 Frontier models cost significantly more than mid-tier or specialized models. 03:12.140 --> 03:15.620 The reality check at the bottom of the slide is especially important. 03:15.980 --> 03:22.260 A seemingly small overhead, such as an extra hundred tokens per request, becomes massive at scale 03:22.740 --> 03:24.420 on 1 million requests. 03:24.540 --> 03:30.060 That translates to 100 million unnecessary tokens and a substantial monthly expense. 03:30.580 --> 03:33.820 This slide reinforces the need for ruthless efficiency. 03:34.020 --> 03:39.660 This slide emphasizes that tokens are the fundamental unit of LM cost. 03:39.860 --> 03:46.980 Every token sent or received costs money, and that cost recurs with every request token. 03:47.020 --> 03:49.940 Optimization is not about trimming words. 03:50.140 --> 03:52.580 It is about architectural efficiency. 03:53.180 --> 03:56.740 The slide outlines concrete ways to reduce token usage. 03:57.100 --> 03:59.380 Writing concise system instructions. 03:59.620 --> 04:06.540 Pruning conversation history to essential context, removing redundant information, and using structured 04:06.540 --> 04:09.490 formats instead of verbose natural language. 04:10.090 --> 04:14.810 Each of these changes reduces recurring costs without degrading quality. 04:15.330 --> 04:21.610 Equally important are the mistakes to avoid repeating instructions in every user message, including 04:21.650 --> 04:27.490 full transcripts when summaries suffice, and requiring verbose formatting that could be handled in 04:27.490 --> 04:28.530 post-processing. 04:28.730 --> 04:30.170 All waste tokens. 04:30.770 --> 04:34.050 The golden rule at the bottom of the slide is worth repeating. 04:34.370 --> 04:39.930 Every unnecessary token is a recurring cost that compounds with every request. 04:40.570 --> 04:44.890 Effective teams audit prompts as aggressively as they audit code. 04:44.930 --> 04:49.810 This slide introduces compression as a precision engineering practice. 04:50.210 --> 04:55.450 The goal is not to cut corners, but to send only what the model truly needs. 04:55.970 --> 05:02.930 The first strategy is summarizing long histories using a smaller, cheaper model to create summaries 05:02.930 --> 05:08.050 can reduce input tokens by 70 to 90% while preserving meaning. 05:08.570 --> 05:15.730 The second strategy is structuring prompts using formats like JSON or templates, which reduce natural 05:15.730 --> 05:18.490 language overhead and improve consistency. 05:18.970 --> 05:24.090 Removing chain of thought from user facing prompts is another major optimization. 05:24.490 --> 05:30.490 While verbose reasoning is useful during development, production systems should request direct answers 05:30.650 --> 05:32.530 unless reasoning is essential. 05:33.130 --> 05:39.970 Finally, encoding static instructions once in the system role instead of repeating them in every request 05:39.970 --> 05:43.530 can immediately cut costs by 20 to 40%. 05:44.010 --> 05:49.090 The best practice note at the bottom reframes prompts as API payloads. 05:49.450 --> 05:54.730 Every sentence, example, and piece of context must justify its existence. 05:55.010 --> 06:00.890 This slide explains why caching is one of the most powerful cost reduction strategies. 06:01.650 --> 06:07.210 Many LM requests are repetitive, making caching and obvious optimization. 06:07.890 --> 06:10.490 The slide lists what to cache aggressively. 06:11.050 --> 06:17.840 Embeddings are expensive to generate but rarely change, making them ideal for indefinite caching. 06:18.240 --> 06:25.960 Repeated prompts such as FAQs often achieve cache hit rates of 40 to 60% in production systems. 06:26.680 --> 06:33.120 Deterministic completions, especially when temperature is set to zero, are predictable and safe to 06:33.160 --> 06:33.720 cache. 06:34.360 --> 06:38.880 Tool results from external APIs are also excellent candidates. 06:39.520 --> 06:42.640 Caching infrastructure operates at multiple layers. 06:43.280 --> 06:50.320 In-memory caches like Reddis for submillisecond access, application level caches for medium frequency 06:50.320 --> 06:54.800 queries, and CDN or edge caches for global distribution. 06:55.480 --> 07:00.440 The key insight is clear most LLM traffic follows the 80 over 20 rule. 07:00.880 --> 07:06.000 Effective caching can reduce API costs by 40 to 70%. 07:06.320 --> 07:11.760 This slide addresses the risks associated with caching while caching saves money. 07:12.000 --> 07:16.070 Improper implementation can serve stale or incorrect responses. 07:16.670 --> 07:19.430 The slide identifies three primary risks. 07:19.950 --> 07:23.150 Stale answers occur when underlying data changes. 07:23.590 --> 07:29.070 Incorrect reuse happens when similar but distinct queries match the same cache entry. 07:29.550 --> 07:34.910 Context mismatch arises when user specific information leaks across requests. 07:35.350 --> 07:38.030 Mitigation strategies are outlined clearly. 07:38.550 --> 07:44.750 Parameterized cache keys should include user ID, session, model, version, and temperature. 07:45.190 --> 07:48.430 Time to live values must reflect content volatility. 07:48.950 --> 07:52.510 Selective caching avoids high risk or personalized outputs. 07:52.990 --> 07:58.310 Cache versioning ensures changes in prompts or models invalidate old entries automatically. 07:58.910 --> 08:05.510 The caching rule at the bottom summarizes the philosophy cache aggressively but intentionally. 08:05.950 --> 08:10.510 Every caching decision is a trade off between cost savings and accuracy. 08:10.950 --> 08:15.510 This slide explains why model selection is a major cost lever. 08:15.900 --> 08:19.980 Not every request requires the most powerful and expensive model. 08:20.420 --> 08:27.180 Strategic matching of model capability to task complexity can reduce costs by 60 to 80%. 08:27.860 --> 08:35.140 The slide breaks down selection criteria, cost per thousand tokens, latency requirements, task capability, 08:35.140 --> 08:36.940 and context window size. 08:37.340 --> 08:43.300 Smaller models are faster and cheaper, making them ideal for real time interactions and simple tasks. 08:43.740 --> 08:49.140 Larger models should be reserved for complex reasoning, planning, and agentic workflows. 08:49.860 --> 08:52.340 The slide provides practical assignment patterns. 08:52.700 --> 08:58.620 Use small models for classification, routing, extraction, summarization, and formatting. 08:58.940 --> 09:04.660 Reserve large models for strategic planning, complex reasoning chains and creative generation. 09:05.060 --> 09:10.700 The core principle is clear task complexity, not habit, should drive model selection. 09:10.940 --> 09:18.980 The final slide introduces dynamic model routing, the most advanced cost optimization strategy discussed 09:18.980 --> 09:19.900 in this section. 09:20.300 --> 09:26.740 Instead of using a single model, production systems analyze each request in real time and routed to 09:26.780 --> 09:28.500 the most appropriate model. 09:29.060 --> 09:36.740 The process involves classifying requests by complexity and user tier, routing them strategically validating 09:36.780 --> 09:42.020 output quality, and escalating to more capable models only when necessary. 09:42.620 --> 09:44.940 Three routing strategies are highlighted. 09:45.220 --> 09:50.580 Complexity based routing uses a lightweight classifier to determine task difficulty. 09:51.100 --> 09:56.740 Confidence based escalation retries with a stronger model only if quality checks fail. 09:57.260 --> 10:04.140 User tier optimization delivers premium experiences where appropriate while controlling costs for free 10:04.140 --> 10:04.660 tiers. 10:05.340 --> 10:12.100 The result is enterprise grade cost control that balances quality, cost and latency dynamically. 10:12.580 --> 10:16.060 The final message reinforces a central theme of this section. 10:16.300 --> 10:20.260 The best systems optimize continuously, not just once.