ai.drawio

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        <mxCell id="N-INbL_eP4r596bO7esT-2" value="&lt;ul&gt;&lt;li&gt;AI use cases&lt;/li&gt;&lt;li&gt;LLMs&amp;nbsp;&lt;/li&gt;&lt;li&gt;domain-specific fine-tuning&lt;/li&gt;&lt;li&gt;data privacy&lt;/li&gt;&lt;li&gt;&amp;nbsp;integration for scalable and secure deployments&lt;/li&gt;&lt;/ul&gt;" style="text;html=1;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
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        <mxCell id="N-INbL_eP4r596bO7esT-3" value="&lt;h1 style=&quot;margin-top: 0px;&quot;&gt;&lt;ul&gt;&lt;li&gt;Common Use Cases&lt;/li&gt;&lt;/ul&gt;&lt;/h1&gt;&lt;ul&gt;&lt;li data-end=&quot;508&quot; data-start=&quot;384&quot;&gt;&lt;p data-end=&quot;508&quot; data-start=&quot;386&quot;&gt;What AI features do you want? (e.g., text summarization, question answering, code generation, chatbots, document analysis)&lt;/p&gt;&lt;/li&gt;&lt;li data-end=&quot;598&quot; data-start=&quot;509&quot;&gt;&lt;p data-end=&quot;598&quot; data-start=&quot;511&quot;&gt;What’s your data domain? (enterprise docs, customer chats, domain-specific terminology)&lt;/p&gt;&lt;/li&gt;&lt;li data-end=&quot;598&quot; data-start=&quot;509&quot;&gt;&lt;p data-end=&quot;598&quot; data-start=&quot;511&quot;&gt;&lt;span style=&quot;background-color: transparent; color: light-dark(rgb(0, 0, 0), rgb(255, 255, 255));&quot;&gt;How will users interact? (API, UI, batch processing)&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;" style="text;html=1;whiteSpace=wrap;overflow=hidden;rounded=0;" vertex="1" parent="1">
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        <mxCell id="N-INbL_eP4r596bO7esT-4" value="&lt;h3 data-end=&quot;802&quot; data-start=&quot;724&quot;&gt;&lt;ul&gt;&lt;li&gt;&lt;span style=&quot;font-weight: normal;&quot;&gt;Option A: Use Pretrained LLMs with Prompt Engineering (Most Common &amp;amp; Fast)&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/h3&gt;&lt;ul data-end=&quot;1391&quot; data-start=&quot;1136&quot;&gt;&lt;li data-end=&quot;904&quot; data-start=&quot;804&quot;&gt;&lt;p data-end=&quot;904&quot; data-start=&quot;806&quot;&gt;Use existing models like OpenAI’s GPT, Anthropic, Cohere, or open-source models (Llama 2, Falcon).&lt;/p&gt;&lt;/li&gt;&lt;li data-end=&quot;904&quot; data-start=&quot;804&quot;&gt;&lt;p data-end=&quot;904&quot; data-start=&quot;806&quot;&gt;&lt;span style=&quot;background-color: transparent; color: light-dark(rgb(0, 0, 0), rgb(255, 255, 255));&quot;&gt;Design prompts to guide the model for your tasks without retraining.&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li data-end=&quot;1057&quot; data-start=&quot;976&quot;&gt;&lt;p data-end=&quot;1057&quot; data-start=&quot;978&quot;&gt;Examples: Provide examples in prompts, set instructions, use few-shot learning.&lt;/p&gt;&lt;/li&gt;&lt;li&gt;Option B: Fine-tune a Pretrained Model on Your Data (Domain Adaptation)&lt;/li&gt;&lt;li data-end=&quot;1204&quot; data-start=&quot;1136&quot;&gt;&lt;p data-end=&quot;1204&quot; data-start=&quot;1138&quot;&gt;Take a base LLM and fine-tune it on your enterprise-specific text.&lt;/p&gt;&lt;/li&gt;&lt;li data-end=&quot;1249&quot; data-start=&quot;1205&quot;&gt;&lt;p data-end=&quot;1249&quot; data-start=&quot;1207&quot;&gt;Requires labeled data or relevant corpora.&lt;/p&gt;&lt;/li&gt;&lt;li data-end=&quot;1291&quot; data-start=&quot;1250&quot;&gt;&lt;p data-end=&quot;1291&quot; data-start=&quot;1252&quot;&gt;Improves model accuracy on your domain.&lt;/p&gt;&lt;/li&gt;&lt;li data-end=&quot;1391&quot; data-start=&quot;1292&quot;&gt;&lt;p data-end=&quot;1391&quot; data-start=&quot;1294&quot;&gt;Use frameworks like Hugging Face Transformers, OpenAI’s fine-tuning API, or tools like LangChain.&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;" style="text;html=1;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
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