AI is rapidly becoming a core part of credit workflows, but it only works as well as the questions you ask. A precise, well-scoped prompt can cut through noise, accelerate analysis, and deliver clearer insights on issuer performance and credit risk.
In this post, we showcase a series of high-impact prompts designed to highlight shifts in credit quality across sectors and regions, helping finance professionals identify emerging trends or shifts in credit sentiment. When scoped carefully (by issuer group, region, or time window) they can highlight divergence across peers or clusters of activity within specific sectors. For portfolio managers, these insights support timely reallocation decisions and offer a foundation for credit risk reporting. For credit analysts, they provide an efficient starting point for deeper fundamental reviews.
Prompts that Work:
Scroll down to view our sample prompts. Each one has been tested and validated by our team to ensure it delivers decision-ready outputs.
Benchmark issuer rating momentum
Chat Workspace
Prompt:
Chart the rating history of Ford Motor Company alongside its major competitors over the past five years.
Why it works:
By opening with “Chart,” the prompt signals the desired format clearly, driving the system to produce a comparative, structured view. Mentioning Ford and its competitors defines the peer set, and the five-year window helps frame longer-term credit trends while excluding irrelevant events. The prompt combines scope, timeframe, and structure in a way that encourages a coherent, benchmark-style output.
Analyze upgrade/downgrade trends over time
Advanced Query Workspace
Prompt:
Determine the ratio of credit rating upgrades to downgrades among European corporates on a YOY basis since 2020.
Why it works:
This prompt asks for a specific quantitative ratio and pairs it with a clear geographic and temporal filter. “YOY basis since 2020” steers the output toward a structured year-by-year format, while “European corporates” ensures the system narrows the universe to the appropriate subset. It’s concise, specific, and repeatable — ideal for trend monitoring and dashboard integration.
Map portfolio exposure by region and credit quality
Advanced Query Workspace
Prompt:
Show the geographical diversification of my @Leveraged Finance Portfolio, including the distribution of credit ratings across regions.
Why it works:
This prompt balances two dimensions, geographic spread and credit distribution, and ties them directly to the user’s portfolio. The reference to “my @Leveraged Finance Portfolio” enables dynamic scoping, while “across regions” and “distribution of credit ratings” signals that a cross-tabulated or multi-axis output is expected. It’s especially effective for visual dashboards and exposure heatmaps.
Track rating activity across a specific sector
Advanced Query Workspace
Prompt:
List the number of rating actions (WR, affirmation, upgrade, downgrade) for @Europe @Pharmaceuticals companies on a YOY basis since 2020. Break this down by entity domicile.
Why it works:
By clearly listing each action type, this prompt reduces ambiguity and instructs the system to categorize results precisely. The use of tags (@Europe @Pharmaceuticals) narrows the focus to a well-defined industry segment, while “break this down by entity domicile” encourages the AI to group results by country, making the output ready for deeper geographic analysis.
Identify recent rating pressure in the U.S. financial sector
Advanced Query Workspace
Prompt:
List @United States @Financial Institutions who have experienced a downgrade over the last three months. Include their current outlook.
Why it works:
This prompt is tightly focused in scope (recent downgrades only) and clearly framed by region and sector. Adding “Include their current outlook” encourages the system to fetch more than static rating data, incorporating forward-looking sentiment where available. It’s highly effective for portfolio monitoring and counterparty screening workflows.
Top Tips for Validating AI-Powered Insights
While well-structured prompts can generate powerful results, validation remains essential. Use the following strategies to ensure outputs are reliable and fit for purpose:
Match to Citations/Official Data: Cross-check AI-generated data against official rating announcements, CreditView entries, or issuer disclosures to confirm accuracy. For Advanced Query responses, validate by checking against the “How is this Generated” section at the bottom of the response. This will explain what data was presented and how.
Lock Down Your Timeframe: Explicitly specify dates or windows in your prompt and verify that timestamps in the response align.
Spot-Check the Numbers: Don’t rely solely on summary figures. Review the raw output to ensure upgrades, downgrades, and other actions are properly counted.
Clarify Terms in Your Prompt: If you're asking about “rating actions,” define what’s included (e.g., WRs, affirmations, outlook changes) to avoid misinterpretation.
Keep Humans in the Loop: AI should accelerate workflows, not replace judgment. Use its outputs as a starting point and apply expert review before taking action.
These examples demonstrate how carefully crafted prompts can transform vast troves of credit data into actionable decision intelligence. Whether analyzing company-specific credit dynamics or monitoring sector-wide trends, the right prompt unlocks the right answer.
Stay tuned for our next installment, where we’ll explore prompts for ratings analyses and movement.
About the author:
Caroline Hedgcock is an Assistant Director of Customer Success at Moody’s, where she specializes in helping clients optimize their use of Moody’s Research Assistant. A leader in AI prompting strategies, Caroline works closely with financial institutions to streamline workflows, accelerate analysis, and unlock deeper insights leveraging Moody’s GenAI-powered Research Assistant.
With extensive expertise in applying advanced prompting techniques, Caroline ensures that clients harness the full capabilities of the Research Assistant to enhance decision-making and operational efficiency. Her approach bridges technical innovation with practical application, helping organizations unlock the benefits of embedding GenAI into their daily workflows.