WEBVTT 00:00.080 --> 00:06.840 For most developers and product teams, interacting directly with large language model weights is neither 00:06.840 --> 00:08.840 practical nor necessary. 00:09.320 --> 00:17.520 Instead, they are accessed through well-designed APIs that expose powerful capabilities while abstracting 00:17.520 --> 00:22.560 away the complexity of model hosting, scaling, and infrastructure management. 00:23.160 --> 00:29.400 These APIs are the critical bridge between advanced AI models and real world applications. 00:30.000 --> 00:36.920 By using LM APIs, developers gain immediate access to state of the art models without worrying about 00:36.960 --> 00:40.800 GPUs, memory management, or distributed systems. 00:41.440 --> 00:48.320 The API handles provisioning, optimization, updates, and reliability, allowing teams to focus on 00:48.320 --> 00:52.800 building user facing features rather than managing backend complexity. 00:54.240 --> 00:57.920 APIs typically enable three core capabilities. 00:58.440 --> 01:06.120 Text generation for completing or transforming content chat based interactions for conversational experiences 01:06.680 --> 01:11.360 and tool or function calling for integrating AI with external systems. 01:12.240 --> 01:19.800 This flexibility makes APIs suitable for everything from simple scripts to large scale production platforms. 01:20.400 --> 01:26.200 Understanding how these APIs work is foundational for full stack AI engineers. 01:27.400 --> 01:34.200 Once you master API interaction, you unlock the ability to embed intelligence into applications quickly, 01:34.360 --> 01:36.440 safely, and at scale. 01:36.640 --> 01:43.000 Text generation APIs represent the simplest and most fundamental way to interact with large language 01:43.000 --> 01:43.640 models. 01:44.120 --> 01:46.320 The interaction pattern is straightforward. 01:46.560 --> 01:52.480 You provide a text prompt as input, and the model generates a continuation of that text as output. 01:53.000 --> 02:00.080 This makes text generation APIs easy to integrate and highly versatile because this interaction is stateless 02:00.080 --> 02:01.000 in one shot. 02:01.040 --> 02:05.000 It works well for tasks where conversation history is not required. 02:05.160 --> 02:12.000 Common use cases include content generation for blogs or marketing copy summarization of long documents. 02:12.200 --> 02:16.800 Translation between languages and code completion for developer productivity. 02:17.360 --> 02:21.760 The simplicity of text generation APIs is also their strength. 02:22.080 --> 02:28.440 They require minimal setup, making them ideal for batch processing, automation, pipelines, and back 02:28.480 --> 02:29.440 end services. 02:29.960 --> 02:36.200 However, because they lack built in memory or conversational context, they are less suitable for multi-turn 02:36.200 --> 02:37.120 interactions. 02:37.760 --> 02:45.120 As an engineer, understanding when to use text generation versus chat based APIs is important. 02:45.640 --> 02:53.360 Text generation excels at focused, isolated tasks, while more complex interactions benefit from conversational 02:53.360 --> 02:54.160 structures. 02:55.080 --> 03:00.920 Choosing the right API type improves both performance and user experience. 03:01.120 --> 03:05.600 While text generation APIs are effective for one off tasks. 03:05.960 --> 03:12.000 Many real world applications require ongoing conversations where context matters. 03:12.760 --> 03:18.800 Chat based completion APIs are designed specifically for these conversational workflows. 03:19.480 --> 03:25.920 They maintain context across multiple turns, enabling more natural and coherent interactions. 03:26.640 --> 03:32.040 Chat API structure input as a sequence of messages rather than a single prompt. 03:32.680 --> 03:38.280 These messages typically include a system message that defines behavior, user messages that represent 03:38.280 --> 03:43.960 queries or instructions, and assistant messages that capture previous model responses. 03:44.560 --> 03:47.600 Together, they create conversational continuity. 03:48.120 --> 03:55.440 This design makes chat APIs ideal for chatbots, customer support agents, AI assistants, and copilots 03:55.440 --> 03:57.000 embedded in applications. 03:57.600 --> 04:04.440 The model can reference earlier parts of the conversation, adapt its tone, and build upon prior responses, 04:04.440 --> 04:07.880 resulting in more reliable and human like interactions. 04:08.720 --> 04:13.440 Another major advantage of chat based APIs is control. 04:13.960 --> 04:20.880 By separating system instructions from user input, engineers can guide model behavior more precisely. 04:21.640 --> 04:27.000 This leads to better alignment, safer outputs, and more predictable responses. 04:27.720 --> 04:33.720 For most interactive applications, chat based completion APIs are the preferred choice. 04:33.960 --> 04:40.920 Chat based APIs rely on a role based message structure that helps the model understand intent, context, 04:40.920 --> 04:42.000 and constraints. 04:42.560 --> 04:47.680 This separation of roles is critical for building reliable and aligned AI systems. 04:48.240 --> 04:51.840 The system role sets the overall behavior of the assistant. 04:52.240 --> 04:57.720 This is where you define the AI's personality, expertise, level, tone, and boundaries. 04:58.480 --> 05:04.280 System messages typically remain fixed throughout the conversation and act as the governing instructions 05:04.280 --> 05:05.600 for all responses. 05:06.400 --> 05:09.840 The user role contains the actual inputs from the end user. 05:10.240 --> 05:15.440 These messages include questions, requests, background information, and follow up probes. 05:15.960 --> 05:21.720 The assistant role represents the model's own responses, which become part of the conversation history 05:21.720 --> 05:24.680 and provide context for future interactions. 05:25.080 --> 05:32.440 Separating these roles improves reliability by distinguishing between what the AI should do and what 05:32.440 --> 05:34.840 the user wants at any given moment. 05:35.440 --> 05:42.400 It also reduces prompt injection risks and improves alignment for full stack AI engineers. 05:42.520 --> 05:49.520 Mastering message roles is essential for building consistent, controllable, and production ready conversational 05:49.520 --> 05:50.240 systems. 05:50.640 --> 05:56.280 Temperature is one of the most important parameters for controlling how an LLM behaves. 05:56.720 --> 06:01.400 It determines how random or deterministic the model's outputs will be. 06:01.840 --> 06:09.090 Effectively controlling the level of creativity in responses at low temperature values, typically between 06:09.090 --> 06:10.920 0 and 0.3. 06:11.320 --> 06:14.960 The model strongly favors the most probable next tokens. 06:15.360 --> 06:19.680 This results in deterministic, focused, and factual outputs. 06:20.080 --> 06:26.280 Low temperatures are ideal for tasks like question answering, code generation, data extraction, and 06:26.280 --> 06:30.640 structured responses, where consistency and accuracy matter. 06:31.120 --> 06:38.760 At medium temperatures around 0.4 to 0.6, the model balances creativity with reliability. 06:39.200 --> 06:45.760 This range works well for general conversation, explanations and content writing where some variation 06:45.760 --> 06:49.240 is useful, but accuracy is still important. 06:49.440 --> 06:57.560 At high temperatures between 0.7 and 1.0, the model explores a wider range of possibilities. 06:57.960 --> 07:02.320 Outputs become more creative, diverse, and sometimes surprising. 07:02.640 --> 07:07.040 This setting is best for brainstorming, ideation, and creative writing. 07:07.440 --> 07:10.880 Choosing the right temperature is a key engineering decision. 07:11.120 --> 07:17.840 Poor temperature choices often explain unpredictable or low quality outputs more than the model itself. 07:17.960 --> 07:25.680 While temperature controls how random the model's choices are, top P, also known as nucleus sampling, 07:25.840 --> 07:30.160 controls which tokens the model is allowed to consider in the first place. 07:30.880 --> 07:37.920 Instead of adjusting randomness globally, top P limits the sampling pool to only the most probable 07:37.920 --> 07:38.640 tokens. 07:39.280 --> 07:40.480 Here's how it works. 07:40.800 --> 07:47.640 The model rakes all possible next tokens by probability, then selects the smallest set whose cumulative 07:47.640 --> 07:51.960 probability is less than or equal to the chosen top p value. 07:52.560 --> 08:00.520 For example, with top p set to 0.9, the model ignores the least likely 10% of tokens and samples only 08:00.520 --> 08:03.320 from the top 90% of probability mass. 08:04.080 --> 08:09.120 This approach adapts dynamically if one token is overwhelmingly likely. 08:09.320 --> 08:11.400 Very few alternatives are considered. 08:11.880 --> 08:16.560 If probabilities are more evenly distributed, more tokens remain in the pool. 08:17.200 --> 08:21.680 This makes top P more context sensitive than fixed random sampling. 08:21.920 --> 08:28.280 Temperature and top P are often used together, but they control different aspects of generation. 08:29.120 --> 08:34.200 A common best practice is to tune one while leaving the other at a sensible default. 08:34.880 --> 08:39.760 Thoughtful use of top P improves output quality and stability. 08:40.240 --> 08:46.600 The max tokens parameter limits how long a model's response can be measured in tokens, rather than 08:46.600 --> 08:47.960 characters or words. 08:48.360 --> 08:55.360 While it may seem simple, this parameter has major implications for cost, latency, and system reliability. 08:55.880 --> 09:02.240 Most LLM APIs charge based on token usage, including both input and output tokens. 09:02.880 --> 09:08.720 Setting appropriate max token limits prevents Events unexpectedly large bills caused by overly verbose 09:08.720 --> 09:09.560 responses. 09:10.040 --> 09:16.000 It also improves latency, since longer outputs take more time to generate and deliver to users. 09:16.600 --> 09:20.400 Max tokens also interact directly with the model's context window. 09:20.800 --> 09:26.120 The combined length of your prompt and the model's response must fit within the available context. 09:26.520 --> 09:31.800 If it exceeds that limit, requests may fail or older context may be truncated. 09:31.960 --> 09:39.240 Finally, Max tokens act as a safety mechanism to prevent runaway generations where the model continues 09:39.240 --> 09:41.320 producing unnecessary text. 09:41.920 --> 09:44.720 Different use cases require different limits. 09:44.840 --> 09:51.880 Summaries may need only 100 to 200 tokens, while longer explanations or essays may require more. 09:52.360 --> 09:59.600 The key takeaway is that API parameters like temperature, top p, and max tokens are not afterthoughts. 09:59.920 --> 10:07.160 They are essential control knobs for building cost effective performance and reliable LM applications.