# Intro to Generative AI series Published articles for Intro to Generative AI series. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Ep. 7: Enhancing AI with Message Chaining and Accuracy Scoring DevFeed: [Ep. 7: Enhancing AI with Message Chaining and Accuracy Scoring]() Original publisher: [Read original article]() Published: 2024-09-19T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Artificial Intelligence](), [Generative AI](), [context](), [AI Chat]() Tags: [advanced-ai-workflows](), [ai](), [ai-accuracy-scoring](), [ai-chatbot-continuity](), [ai-context-management](), [ai-conversation-tracking](), [ai-decision-making-validation](), [ai-driven-system-accuracy](), [ai-for-healthcare-accuracy](), [ai-legal-and-finance-accuracy](), [ai-message-chaining](), [ai-model-chaining](), [building-smarter-ai-systems](), [chatbots](), [complex-ai-tasks](), [context](), [context-aware-ai-systems](), [enhancing-ai-conversations](), [factuality-scoring-in-ai](), [generative-ai](), [generative-ai-techniques](), [intro-to-generative-ai-series](), [models](), [multi-faceted-ai-automation](), [multi-step-ai-processes](), [processes](), [reliable-ai-outputs](), [workflows]() ### AI overview This episode explains how message chaining preserves conversation context, how factuality scoring checks AI responses against verified sources, and how chaining multiple models enables complex, multi-step workflows. ### Source excerpt Introduction: Welcome to the final episode of our Intro to Generative AI series! In this episode, Daniel Whitenack takes the concepts you've been learning and shows you how to apply advanced techniques like message chaining and factuality scoring to make your AI-driven systems smarter and more reliable. This session will help you understand how to create workflows that combine multiple models, ensuring your AI can provide accurate, context-aware responses and make decisions grounded in real data. ## Ep. 4: Streamlining Prompt Engineering and Context Handling in Generative AI DevFeed: [Ep. 4: Streamlining Prompt Engineering and Context Handling in Generative AI]() Original publisher: [Read original article]() Published: 2024-08-08T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Prompt Engineering](), [context](), [AI Models](), [Command-line interface]() Tags: [ai](), [ai-case-studies](), [ai-command-line-interfaces](), [ai-context-management](), [ai-context-switching](), [ai-contextual-responses](), [ai-development-techniques](), [ai-input-loops](), [ai-integration-strategies](), [ai-model-optimization](), [ai-models](), [ai-prompt-templates](), [building-ai-systems](), [command-line](), [context](), [context-aware-ai](), [dynamic-prompt-templating](), [efficient-ai-interactions](), [engineering](), [generative](), [generative-ai](), [interactive-ai-systems](), [intro-to-generative-ai-series](), [managing-multiple-contexts-in-ai](), [prompt](), [prompt-engineering](), [real-time-ai-interactions](), [switching](), [terminal-input-in-ai]() ### AI overview Episode 4 of an introductory Generative AI series explains prompt templating, context management, and interactive AI systems. It demonstrates using dynamic prompt templates, incorporating context from files, switching between contexts, and accepting terminal input and command-line arguments. ### Source excerpt Introduction: Welcome to Episode 4 of our Intro to Generative AI series! In this episode, Daniel dives into the essential technique of prompt engineering, focusing on creating dynamic and interactive prompts to enhance the capabilities of AI models. Prompt Templating: Techniques for creating and using dynamic prompt templates to enhance AI interactions. Context Management: Strategies for integrating and switching between multiple contexts in AI applications. Interactive Systems: Building AI systems that respond to user inputs in real-time, using terminal input loops and command-line arguments. ## Ep. 2: Mastering LLM Integration with Go and Prediction Guard DevFeed: [Ep. 2: Mastering LLM Integration with Go and Prediction Guard]() Original publisher: [Read original article]() Published: 2024-07-06T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Go Language](), [Large Language Model](), [Prompt Engineering](), [API](), [Generative AI]() Tags: [access-hosted-language-models](), [advanced-ai-capabilities](), [ai](), [ai-project-innovation](), [ai-response-variability](), [ai-tools-for-developers](), [api](), [configure-max-tokens-and-temperature](), [customizing-ai-model-output](), [effective-ai-prompts](), [episode-2-generative-ai](), [fine-tuning-ai-responses](), [generate-diverse-ai-responses](), [generative-ai](), [generative-ai-tutorial](), [go](), [go-client-for-prediction-guard](), [go-client-setup-tutorial](), [go-developers-and-ai](), [go-programming-language-ai](), [hosted-ai-models-setup](), [integrate-llms-into-projects](), [intro-to-generative-ai-series](), [learn-ai-with-go](), [leverage-large-language-models](), [llm](), [manage-ai-output-variability](), [practical-ai-integration](), [practical-ai-with-go-language](), [prediction-guard-api-tutorial](), [prompt-engineering](), [prompt-engineering-guide](), [step-by-step-ai-tutorial](), [temperature-settings-in-ai](), [using-llms-with-go](), [using-prediction-guard-api]() ### AI overview This episode explains how to integrate large language models into Go applications using the Go client for Prediction Guard. It covers connecting to hosted models, designing prompts, configuring max tokens and temperature, and managing variability in generated responses. ### Source excerpt Introduction: Welcome to Episode 2 of our Intro to Generative AI series! In this segment, Daniel dives into the practical aspects of working with large language models (LLMs) using the Go programming language and the Prediction Guard API. Accessing LLMs: Learn how to set up and connect to hosted models using the Go client for Prediction Guard. Prompt Engineering: Discover how to create effective prompts and configure parameters like max tokens and temperature. Output Variability: Understand how to manage and utilize variability in AI responses for different results. ## Ep. 1: Enhancing Your Go Projects with Generative AI: Exploring LLMs DevFeed: [Ep. 1: Enhancing Your Go Projects with Generative AI: Exploring LLMs]() Original publisher: [Read original article]() Published: 2024-06-20T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Go Language](), [Large Language Model](), [Generative AI](), [AI-assisted coding](), [llama](), [Code]() Tags: [ai-driven-text-based-tasks](), [ai-enhanced-go-workflows](), [ai-for-autocomplete](), [ai-for-code-translation](), [ai-for-customer-service-automation](), [ai-for-document-summarization](), [ai-for-interactive-applications](), [ai-for-sql-query-generation](), [ai-generated-code-completions](), [ai-in-customer-support](), [ai-in-programming](), [code-generation](), [code-generation-with-ai](), [codium-ai-integration](), [daniel-s-ai-tutorial-series](), [enhancing-coding-efficiency-with-ai](), [generate](), [generative-ai](), [generative-ai-for-developers](), [generative-ai-in-go-projects](), [github-copilot-alternatives](), [go](), [go-programming-with-ai](), [go-projects](), [harnessing-ai-in-go-programming](), [integrating-llms-in-go](), [intro-to-generative-ai-series](), [large-language-models-llms](), [llama-3-model](), [llms](), [llms-architecture-and-functionality](), [llms-in-code-editors](), [practical-applications-of-llms](), [predictive-text-ai-models](), [text-generation-with-ai](), [understanding-llms]() ### AI overview The first episode of an introductory series explains how large language models such as Llama 3 work and how developers can integrate them into Go projects. It covers text generation, autocomplete, code generation, customer service automation, SQL generation, code translation, and document summarization. ### Source excerpt Introduction: Welcome to the first episode of our "Intro to Generative A.I" series! In this episode, Daniel dives into the intriguing world of large language models (LLMs), providing a comprehensive understanding of how these powerful tools work and their practical applications. Gain insights into the architecture and functionality of large language models like Llama 3, and how they process and generate language-based responses. Learn how these models can be integrated into Go projects, enhancing capabilities like autocomplete, code generation, and more through practical examples and demonstrations.