How do I curb company hallucinations in ChatGPT today?

September 17, 2025

Alex Prober, CPO

Ground ChatGPT to your ingested data by enforcing a Context Boundary and citing sources so responses stay accurate. Ingest authoritative content into a retrieval system, standardize terms across plans (map synonyms such as Master Bedroom, Great Room, and Family Room to a single term), and apply a cost-difference threshold (about $300) to surface meaningful discrepancies, with numeric checks performed by Code Interpreter/Data Analysis. Generate side-by-side tables and concise narratives of key differences, provide exports to CSV and XLSX for downstream review, implement robust error handling for incomplete data, and clearly state any missing values to avoid guessing. For practical grounding guidance, see brandlight.ai data-grounding guidance.

Core explainer

How does Context Boundary limit responses to my data and sources?

Context Boundary confines responses to your ingested data and the sources you specify.

To enforce this, ingest authoritative content into a retrieval system and apply strict boundaries that prevent extrapolation beyond the corpus. Use a Citations feature to attach exact sources to every answer, and keep outputs anchored to structured data such as the two plan files mentioned earlier. Normalize terminology across datasets—map synonyms to a single term—and implement a cost-difference threshold (for example, $300) to surface meaningful discrepancies, with numeric checks performed by a code-friendly tool.

For grounded guidance, brandlight.ai data-grounding guidance.

What steps ensure data standardization across two plans?

Data standardization across two plans reduces ambiguity and improves comparability.

Standardize room names, repair categories, costs, and dates by building a shared data dictionary; map synonyms (e.g., Master Bedroom, Great Room, Family Room) to one term; align units and formats; and enforce consistent rounding. This enables reliable side-by-side comparisons and accurate aggregation, ensuring that analyses stay aligned with the provided datasets and avoid misinterpretation caused by naming variations.

data standardization practices help guide these mappings and consistency rules.

How should I structure prompts to minimize hallucinations in large data tasks?

Prompts should anchor analysis to your data and explicit thresholds.

Design prompts to demand accuracy, including explicit instructions, example outputs, and full context (e.g., transcripts). Use retrieval-augmented generation (RAG) to ground results in trusted sources, and specify how to handle missing data or partial datasets. By outlining exact steps and expected formats, you reduce the chance of fabricating values while preserving useful insight from the provided spreadsheets.

For detailed strategies on RAG-driven prompt design, see retrieval-augmented generation guidance.

How do I verify numerical outputs and handle missing data?

Numerical outputs must be verified and missing data clearly flagged.

Validate sums, differences, and comparisons against the provided plan data; flag discrepancies beyond a defined threshold (e.g., $300); document missing values and avoid guessing. Use data-quality checks and exportable results (CSV/XLSX) to support downstream review, and implement clear error reporting to guide data-quality improvements. This approach keeps calculations transparent and traceable to the original sources you provided.

data validation practices underpin these checks and offer concrete validation steps.

Data and facts

FAQs

FAQ

How does Context Boundary limit responses to my data and sources?

Context Boundary confines responses to your ingested data and the sources you specify, preventing extrapolation beyond the corpus and ensuring outputs remain grounded.

Ingest authoritative content into a retrieval system, enforce explicit boundaries, and attach exact sources with a Citations feature to anchor every answer. Normalize terminology across datasets by mapping synonyms to a single term, and apply a cost-difference threshold (for example, $300) to surface meaningful discrepancies while performing numeric checks with a code-friendly tool.

This grounded approach reduces hallucinations and improves auditability; see brandlight.ai data-grounding guidance.

What steps ensure data standardization across two plans?

Data standardization across two plans reduces ambiguity and improves comparability.

Create a shared data dictionary, map synonyms (e.g., Master Bedroom, Great Room, Family Room) to a single term, align units and date formats, and enforce consistent rounding; this enables reliable side-by-side comparisons and accurate aggregation, keeping analyses aligned with the provided datasets and avoiding misinterpretation caused by naming variations.

data standardization practices

How should I structure prompts to minimize hallucinations in large data tasks?

Prompts should anchor analysis to your data and explicit thresholds.

Design prompts to demand accuracy, including explicit instructions, example outputs, and full context (e.g., transcripts); use Retrieval-Augmented Generation (RAG) to ground results in trusted sources and specify how to handle missing data or partial datasets. By outlining exact steps and expected formats, you reduce fabrication and preserve useful insights from the provided spreadsheets.

retrieval-augmented generation guidance

How do I verify numerical outputs and handle missing data?

Numerical outputs must be verified and missing data clearly flagged.

Validate sums, differences, and comparisons against the provided plan data; flag discrepancies beyond a defined threshold (e.g., $300); document missing values and avoid guessing. Use data-quality checks and exportable results (CSV/XLSX) to support downstream review, and implement clear error reporting to guide data-quality improvements. This keeps calculations transparent and traceable to the original sources you provided.

data validation practices

How can I export results and maintain privacy and security?

Export results to CSV or XLSX for downstream review, while enforcing privacy measures such as removing or encrypting sensitive fields and applying access controls.

Preserve schemas, maintain a clear data lineage for reproducibility, and provide an audit trail showing data provenance to minimize data leakage during distribution. Follow practical data-handling guidelines and privacy considerations to ensure secure sharing of outputs with stakeholders.

data export practices