The Importance of Prompt Engineering: What You Ask Matters

Prompt engineering

You know the AI adoption curve is well underway when the wow factor starts giving way to the how factor. A great example of this is prompt engineering, which has to do with how to optimize the process for querying or directing AI systems to do their work and generate results. As financial organizations get more comfortable with AI in their IT operations, they should also get more familiar with prompt engineering as a way to reap maximum value and reliability from their AI systems.


Prompt Engineering Addresses Both the Benefits and Risks of AI

Whether it’s ChatGPT or other generative AI that taps into publicly facing data, or traditional AI deployments working on expanded data architectures within the enterprise, AI has fundamentally changed the nature of search and IT command structures. Larger datasets and more probabilistic models have rendered obsolete the many older frameworks for querying systems that were designed for limited datasets and rules-based models. 

Against this backdrop, prompt engineering is designed to address both the benefits and the risks of AI. Traditional prompts and search protocols may not adequately leverage the algorithmic insights or autonomous workflow capabilities that a modern AI system is capable of – leaving knowledge gaps and untapped value. A chief benefit of prompt engineering here is to evolve query protocols and commands to adequately leverage AI’s expanded capabilities and broad reach across vast public and private datasets. 

The flip side is that prompt engineering is also needed to cut through all the noise that’s generated from this expanded reach – noise that can obscure results with excessive, inaccurate or extraneous information. This is especially true with AI applications that are increasingly probabilistic vs. rules-based; they are prone to more error as unsupervised, probabilistic models essentially “guess” at results if a query is not crafted clearly enough. 

ECI Prompt Engineering Helps Maximize ROI AI Value and Minimize AI Risk 

ECI’s unique blend of AI expertise and deep vertical familiarity with alternative investment firms makes us the MSP partner of choice when it comes to prompt engineering in the financial sector space. We understand that prompt engineering is more than just typing in a search question; it also requires a deeper understanding on how LLMs look for semantic relevance, and how query structures can tap into the most relevant data and most valuable insights. 

For instance, we apply a highly-specialized retrieval-augmented generation (RAG) framework for clients to fine tune AI applications with the business and regulatory context that teaches a model how it should respond to certain query patterns. We can also configure systems with “chain of thought” programming that overlays your thought process onto the AI model that governs how a search or business function should be conducted; this leads to more accurate and business-relevant results.

Critically, ECI can also program an AI system to share its own “chain of thought” in generating results or making recommendations. This adds a level of traceability that is essential for financial sector organizations to demonstrate compliance and remain accountable to regulators for enterprise decisions – whether those decisions were made by a human analyst or an AI system. 

Our prompt engineering expertise is now even easier to access as part of the new ECI Large Language Application (ELLA) managed platform that we’ve just unveiled. ELLA serves as the foundation for ECI’s managed environment for secure, governed and compliant AI – including how prompt engineering can help generate stronger AI analysis, market evaluation and bespoke recommendations for decision support. 

Learn more about our prompt engineering expertise and other ways the ECI team can help you get the most value from your AI investments by contacting


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