Why developers, GraphRAG, and Open Source Should be Core to Your GenAI strategy

The impact of GenAI and the explosion of new technologies it is facilitating may be unparalleled. Global investment in AI and GenAI is predicted to surge past $12 billion by the end of the year. It’s also seeing exponential adoption – 1 out of 3 companies already use GenAI in at least one business function. Yet what sets GenAI apart from the emergence of previous major technologies is the central role that developers are playing.

Developers played an important but supporting role in shaping the AI boom. But by contrast, they’re now finding themselves front and center with the future of GenAI. It’s a major and current shift, and the successful CIO needs to understand what it means and how to adapt.

Technical expertise is essential for GenAI visionaries

Many innovations are vision-driven. Apple founder Steve Jobs was clear in setting the agenda and directing distributed teams within Apple to make it happen, and Tim Cook continues this innovation strategy.

Yet innovation can also drive vision when developers are involved, an example of which is LangChain. Founded in 2022, it underwent rapid change as hundreds of developers contributed. From it emerged LangChain Expression Language, an easy declarative way to streamline text processing and interaction with LLMs. Today, it empowers data engineers with comprehensive tools for using LLMs in different applications. LangChain’s vision – to simplify the creation and production of LLM applications – came out of these innovations, and developers continue to experiment and feed more changes into the system.

Horizontal technologies often grow this way. Their power comes by putting them in the hands of those who can explore and discover their possibilities. However, what sets GenAI apart is that it is both deeply technical and quickly evolving. To unlock its potential, builders must be deeply technical by necessity. We can dream about GenAI’s possibilities, but making them happen requires understanding what’s possible and why.

GraphRAG and Open Source models: the real GenAI needle movers

The second reason why developers are driving GenAI innovation is the emergence of GraphRAG, the combination of Knowledge Graphs and Retrieval Augmented Generation (RAG), along with open-source models.

GraphRAG enhances traditional retrieval methods (RAG) used by LLMs by integrating knowledge graphs, which structure data into interconnected entities. You actually need both to achieve results that are accurate, contextually rich, explainable, and transparent. Without GraphRAG, most enterprise use cases for Large language models (LLMs), like those powering ChatGPT, Google Gemini, and other chatbots, are limited. RAG and GraphRAG improve the quality of responses by retrieving data from external knowledge sources, ensuring more accurate, reliable, and up-to-date results. Both have become indispensable for enterprises,  giving LLMs a logical way to access and leverage their enterprise data.

GraphRAG and RAG also enable developers to treat LLMs as one (or, more commonly, more than one) part of a more extensive GenAI pipeline. GenAI technology may be new, but its components-based approach is something developers intuitively understand more than anyone. When paired with knowledge graphs through GraphRAG the results add knowledge to GenAI and take it to the next level.

The rise of open-source GenAI models is similarly empowering developers. They further invite the possibility of dividing GenAI problems into separate parts for better and cheaper problem-solving, where each model can play a specific role inside larger sets of activities. As just one example, developers are already using one model to help write code and another model to help write tests for training GenAI systems.

The unstoppable march of the developer

The final reason lies in the vast number of developers. Developers now number in the millions – 28.7 million of them, in fact, and increasing each year. It’s no coincidence that more top-performing CEOs have engineering degrees than MBAs.

Developers are a vital bellwether for GenAI’s technical viability and value within their organisations. GenAI is also costly now, which won’t change for a while – but costs are starting to drop. For example, the rise of smaller models specialised for specific tasks and domains is beginning to lower barriers to access for many organisations. The result is creating more developers, not less. This is backed up by the US Bureau of Labor: it predicts that developer-driven jobs will grow by 25% through 2032, much faster than the average for all other occupations.

Key actions IT leaders can take:

1) Give developers the freedom to experiment, even if it’s an hour of their workday.

Developers need to find out what they can and can’t do. Innovation only happens with experimentation. We co-created a pre-built GenAI stack with Docker, LangChain, and Ollama specifically for this purpose. It’s just one of many great tools out there to try.

Ensure you give developers frameworks that remove creativity barriers and facilitate safe and responsible experimentation. Build clear policies into these frameworks, easy access to the latest tech and tools, and data privacy and security parameters. At Neo4j, we give our developers time for side projects, dedicated lab days, and meet-ups so they can play with new things in different ways, all within pre-agreed budgets and time frames. Magic can’t happen without curiosity.

2) Empower developers to reach GenAI goals, objectives, and competencies.

Building a GenAI application is one thing. Ensuring its accuracy, transparency, and explainability is another. CIOs need to architect and scale with these must-have goals in mind. Knowledge graphs have emerged as an essential component for GenAI, grounding LLMs in facts while preventing hallucinations. CIOs will want to align with developers on these, which are vital for GenAI adoption.

EY suggests that leaders should also consider prioritising small strategic initiatives that link separate or independent teams in ways that allow multiple uncertainties or constraints to be addressed simultaneously. The result can help bridge the gap between a company’s current state and its desired future state. Leaders assessing where to act now vs what to decide later can also test decisions with developers, validating executive decisions with frontline input.

3) Think about the developer experience, not just their productivity.

A corporation with 30,000 developers using software costing £9.99 per month per developer is right in wanting efficiency gains. However, a company with ten developers would be better off focusing on initiatives that involve their own top line as opposed to the bottom. Developers do far more than write lines of code. They tear software apart. They diagnose, debug, and fix it. Unlike even the best automation tools, they make software do what humans want and need to do.

CIOs have the opportunity to prioritise efficiencies with GenAI and build innovations that make a meaningful difference to the top line. Bottom-line efficiencies are important, can present low-hanging fruit, and have employees as end users or in the loop to represent a safe zone to experiment. That said, the ultimate winners will be those who find opportunities to use top-line innovation to win using GenAI. Just ask any of the world’s most valuable companies today.

Closing thoughts on GenAI

Developers are your frontline builders and core partners in the GenAI journey. They’re the engine room of GenAI’s advances, creating new solutions, and sit at the coalface of its responsible and ethical development. The opportunity is enormous, and it’s one CIOs can’t ignore.

By Philip Rathle, CTO, Neo4j.