WEBVTT 00:00.080 --> 00:06.600 In today's rapidly evolving AI landscape, not all large language models are created equal. 00:07.040 --> 00:12.920 While many models may appear similar on the surface, they differ significantly in how they reason, 00:12.920 --> 00:18.080 how they generate code, how they handle safety, and how they scale in production. 00:18.520 --> 00:25.840 Understanding LM families is critical because the model you choose directly shapes your products performance, 00:25.880 --> 00:28.400 reliability, and cost structure. 00:28.680 --> 00:35.640 Some models excel at complex reasoning and problem solving, while others are optimized for coding assistance 00:35.640 --> 00:38.040 or long form document understanding. 00:38.480 --> 00:45.480 Certain models prioritize safety and alignment, making them suitable for sensitive applications, while 00:45.480 --> 00:49.320 others focus on efficiency and performance per parameter. 00:49.840 --> 00:52.200 These differences are not academic. 00:52.400 --> 00:58.240 They affect response latency, infrastructure requirements, and long term maintainability. 00:58.400 --> 01:04.900 Choosing the right LM is fundamentally an engineering decision, not a popularity contest. 01:05.420 --> 01:12.100 Benchmarks and hype can be misleading if they don't align with your actual use case as a full stack 01:12.180 --> 01:13.260 AI engineer. 01:13.380 --> 01:20.460 Your goal is to select a model that fits your constraints, integrates well with your system, and supports 01:20.460 --> 01:22.580 your long term product strategy. 01:23.380 --> 01:27.020 This section will help you make those decisions confidently. 01:27.140 --> 01:35.580 Proprietary or closed source LA are typically offered through managed APIs hosted by major AI providers. 01:36.300 --> 01:42.980 These models are maintained entirely by the vendor, meaning updates, security patches, performance 01:42.980 --> 01:46.700 improvements, and scaling concerns are handled for you. 01:47.300 --> 01:53.420 This makes them extremely attractive for teams that want to move quickly from idea to production. 01:53.660 --> 01:59.300 One major advantage of closed source models is their strong out of the box performance. 01:59.500 --> 02:06.710 They are usually trained on massive data sets, fine tuned extensively and equipped with built in safety 02:06.710 --> 02:08.110 and moderation layers. 02:08.670 --> 02:15.350 This reduces the engineering burden and lowers the risk of unsafe or unpredictable outputs. 02:15.830 --> 02:20.710 Developers can focus on application logic rather than model management. 02:21.150 --> 02:24.110 However, these benefits come with trade offs. 02:24.310 --> 02:29.550 Closed source models often involve per token costs that scale with usage. 02:29.710 --> 02:36.830 Limited customization options and some degree of vendor lock in data privacy and compliance requirements 02:36.830 --> 02:39.910 may also restrict their use in certain industries. 02:40.190 --> 02:45.790 For many teams, closed source llms are the best choice for rapid prototyping. 02:45.830 --> 02:52.750 Early stage products and production systems where speed and reliability matter more than full control. 02:52.750 --> 03:00.230 The GPT family, developed by OpenAI, is one of the most widely adopted and influential LLM families 03:00.230 --> 03:01.310 in the industry. 03:01.870 --> 03:07.650 These models are known for their strong reasoning capabilities, high quality code generation, and 03:07.650 --> 03:10.890 robust support for tool usage and function calling. 03:11.290 --> 03:19.010 As a result, GPT models are commonly used in chatbots, AI copilot's, enterprise automation, and 03:19.010 --> 03:20.890 content generation systems. 03:21.370 --> 03:26.890 One of the biggest strengths of the GPT ecosystem is its developer experience. 03:27.130 --> 03:32.610 The APIs are mature, well documented, and supported by a large community. 03:33.010 --> 03:37.410 This makes integration straightforward and accelerates development. 03:37.770 --> 03:45.610 GPT models also receive regular updates, improving performance and expanding capabilities over time. 03:45.850 --> 03:49.130 However, there are important trade offs to consider. 03:49.490 --> 03:56.890 API costs can become significant at scale, especially for applications with high traffic or long context 03:56.890 --> 03:57.490 usage. 03:57.970 --> 04:05.030 Customization options are limited compared to open source Alternatives, and organizations must consider 04:05.030 --> 04:07.710 data privacy and vendor dependency. 04:08.110 --> 04:15.350 Overall, the GPT family is an excellent choice for teams that need strong general purpose performance, 04:15.510 --> 04:19.710 fast iteration, and reliable production grade infrastructure. 04:20.830 --> 04:28.190 The Claude and Gemini families represent two distinct approaches to proprietary LLM design, each optimized 04:28.190 --> 04:29.630 for different priorities. 04:30.030 --> 04:36.190 Claude, developed by anthropic, emphasizes safety, alignment, and careful reasoning. 04:36.630 --> 04:43.950 It is built using constitutional AI principles, which aim to reduce harmful outputs and improve reliability 04:43.950 --> 04:45.630 in sensitive contexts. 04:46.030 --> 04:52.710 Claude models are particularly strong at long context understanding, with the ability to process very 04:52.710 --> 04:53.950 large documents. 04:53.990 --> 05:01.070 This makes them well suited for tasks like document analysis, summarization, compliance review, and 05:01.070 --> 05:02.550 content moderation. 05:03.120 --> 05:10.640 They also tend to provide clear, step by step explanations, which is valuable in educational and enterprise 05:10.640 --> 05:11.320 settings. 05:12.120 --> 05:19.640 Gemini, developed by Google, focuses heavily on multimodal reasoning and ecosystem integration. 05:20.080 --> 05:27.560 Gemini models are designed to handle text, images, and other modalities within a unified architecture. 05:28.000 --> 05:35.640 Their tight integration with Google Search, cloud and productivity tools makes them especially powerful 05:35.640 --> 05:39.360 for knowledge work and search augmented applications. 05:39.920 --> 05:48.000 Choosing between Claude and Gemini often comes down to priorities, safety and long context, reasoning 05:48.160 --> 05:52.680 versus multimodal capability and ecosystem integration. 05:52.920 --> 05:58.600 Open source llms represent a fundamentally different approach to deploying AI systems. 05:58.920 --> 06:05.860 Instead of accessing models through managed APIs, teams can download model weights, deploy them locally 06:05.860 --> 06:09.300 or in the cloud and fully control how they are used. 06:09.740 --> 06:16.460 This provides unmatched flexibility, transparency and data sovereignty with open source models. 06:16.580 --> 06:23.180 Engineers can fine tune models for specific domains, customize behavior, and optimize performance 06:23.180 --> 06:24.940 for their exact use case. 06:25.140 --> 06:31.860 This is particularly valuable for data sensitive applications or organizations with strict compliance 06:31.860 --> 06:32.820 requirements. 06:33.380 --> 06:40.580 Open source LMS also enable long term cost optimization by avoiding per token API fees. 06:40.860 --> 06:43.900 However, this flexibility comes at a cost. 06:44.180 --> 06:50.580 Open source models require significant machine learning expertise, robust infrastructure for training 06:50.580 --> 06:53.340 and inference, and ongoing maintenance. 06:53.780 --> 06:58.940 Teams must handle scaling, monitoring, updates, and security themselves. 06:59.140 --> 07:02.940 For organizations with strong engineering capabilities. 07:03.220 --> 07:11.760 Open source LMS unlock powerful possibilities for others, they may be better suited as a second step 07:11.760 --> 07:18.200 after validating a product using closed source APIs among open source LMS. 07:18.320 --> 07:23.400 The llama, Mistral and Falcon families stand out as leading options. 07:23.760 --> 07:30.240 Llama, developed by meta, has become a standard foundation for research and fine tuning experiments. 07:30.920 --> 07:37.920 It offers well-documented architectures, strong base models across multiple sizes, and a massive community 07:37.960 --> 07:38.880 ecosystem. 07:39.320 --> 07:44.640 Many popular derivatives, such as alpaca and vicuna are built on llama. 07:44.920 --> 07:48.840 Mistral focuses on efficiency and performance per parameter. 07:48.880 --> 07:55.240 These models deliver competitive results while requiring fewer computational resources, making them 07:55.240 --> 07:59.880 attractive for cost sensitive deployments and low latency applications. 08:00.440 --> 08:04.500 Their permissive licensing also makes them suitable for commercial use. 08:05.020 --> 08:11.940 Falcon models developed by the Technology Innovation Institute provide truly open access, large language 08:11.940 --> 08:15.700 models trained on high quality, diverse datasets. 08:15.740 --> 08:22.020 They are known for strong, multilingual support and solid performance in text generation and reasoning 08:22.060 --> 08:22.700 tasks. 08:23.540 --> 08:26.260 Each of these families serves different needs. 08:26.460 --> 08:30.180 Llama is ideal for experimentation and customization. 08:30.300 --> 08:37.140 Mistral for efficient production systems and Falcon for open access, multilingual applications. 08:37.460 --> 08:41.140 Choosing the right one depends on your constraints and goals. 08:41.460 --> 08:48.060 Selecting the right LLM is a multidimensional engineering decision that requires careful analysis of 08:48.060 --> 08:50.500 your system requirements and constraints. 08:50.860 --> 08:53.380 There is no universally best model. 08:53.620 --> 08:57.260 Only models that are better suited for specific use cases. 08:57.900 --> 09:00.660 Start by clearly defining your application. 09:01.100 --> 09:08.110 Are you building a chatbot a code assistant a retrieval augmented system or an autonomous agent. 09:08.710 --> 09:15.830 Next, consider data sensitivity and compliance requirements if your application handles private or 09:15.830 --> 09:17.030 regulated data. 09:17.350 --> 09:21.350 Open source or self-hosted solutions may be necessary. 09:21.870 --> 09:23.870 Budget is another key factor. 09:24.430 --> 09:30.470 Closed source APIs offer speed and convenience, but can become expensive at scale. 09:31.070 --> 09:37.310 Open source models require upfront infrastructure investment but may reduce long term costs. 09:37.830 --> 09:42.710 Latency requirements also matter, especially for real time systems. 09:43.350 --> 09:50.190 A common strategy is to start fast with closed source APIs for prototyping and validation, then transition 09:50.190 --> 09:53.230 to open source models as requirements mature. 09:53.790 --> 10:00.790 The key takeaway is simple the best model is not the most powerful or popular, it's the one that best 10:00.790 --> 10:05.550 fits your use case constraints and organizational capabilities.