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# Should we build Generative AI into our existing software?

Stop bolting Generative AI onto your software just to satisfy investor hype. Master practical implementations like RAG and learn how technical leaders can guide stakeholders through the noise.

- **Speakers:** [Simon Müller](https://www.wearedevelopers.com/@simon-muller)
- **Event:** World Congress 2024
- **Published:** August 20, 2024
- **Duration:** 24:51
- **URL:** https://www.wearedevelopers.com/videos/1129-should-we-build-generative-ai-into-our-existing-software

## Summary

Applying Betteridge's law of headlines to the question of whether to build Generative AI into existing software initially yields a firm no, highlighting how organizations frequently bolt on AI to satisfy hype rather than foundational product principles like desirability, viability, and feasibility. However, for functions inherently tied to language and interaction, such as customer service, data entry, and scaled marketing, integrating large language models (LLMs) is rapidly shifting into a strategic necessity. Navigating this transition forces developers and tech teams into a chicken-and-egg scenario where business stakeholders propose use cases based on investor pressure, and engineering teams must anchor those demands with technical reality. When structuring these implementations, technical leaders should default to proven methodologies, relying on retrieval-augmented generation (RAG) paired with vector databases as the practical architectural choice for document interaction, while explicitly reserving fine-tuning for niche use cases that require highly specialized vocabularies. As AI architecture continues to evolve vertically and horizontally, hyperscaler pipelines like Azure AI Search are simplifying implementation, while the core underlying transformer models are successfully expanding beyond raw text into predictive time-series data and IoT forecasting. Functionally, generative AI is maturing past manual prompting tools into embedded coding assistants, aggressively laying the groundwork for complex multi-agent systems via frameworks like CrewAI and BabyAGI. Ultimately, the engineering mandate is to look beyond the hype cycle, form robust strategic mental models, and act as a reliable translating partner for business leaders navigating a noisy technological landscape.

**Keywords:** generative ai integration, product viability frameworks, retrieval-augmented generation, RAG architectures, LLM fine-tuning, vector databases, technology radars, time series predictive modeling, autonomous ai agents, multi-agent systems, crewai frameworks, babyagi implementations, azure ai search, transformer architectures, ai-driven customer support, technology strategy evaluation

## Chapters

1. **Applying Betteridge's law of headlines to generative AI** (00:07) — Exploring whether generative artificial intelligence should be built into software starts with understanding different product approaches.
1. **Evaluating generative AI against product viability and desirability** (02:03) — Assessing product ideas requires proving desirability, viability, and feasibility before integrating hyped technologies.
1. **Finding definite use cases for generative AI integration** (05:41) — Certain business areas like customer service and translation are prime targets where artificial intelligence integration makes immediate sense.
1. **Navigating investor pressure for AI in product development** (09:16) — Product decisions are often influenced by investor demands to include artificial intelligence capabilities to secure funding.
1. **Guiding business stakeholders with technology radars and knowledge** (11:13) — Developers must guide business decisions by tracking technology maturity and assessing when to adopt new frameworks.
1. **Demystifying retrieval augmented generation and fine-tuning models** (14:24) — Explaining established architecture patterns helps stakeholders understand when to use context retrieval versus model fine-tuning.
1. **Tracking cloud provider simplifications and time series models** (16:43) — Following the evolution of generative architectures reveals new applications like time series forecasting and fully managed cloud solutions.
1. **Evolving generative technology from basic tools to autonomous agents** (19:14) — Interaction paradigms are shifting from manual tools to pair-programming assistants and eventually building multi-agent autonomous systems.
1. **Partnering with business leaders to build meaningful software products** (22:17) — Technologists must form mental models of emerging technologies to effectively support and ground eager business stakeholders.

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