DataEngUtils for AI Engineers
AI Engineers spend a lot of time on the seams of an application: inspecting authentication tokens, validating JSON payloads, checking structured output against a schema, and encoding request bodies. Every integration with a model API involves a JWT to decode, a JSON response to format, or a base64 payload to inspect — and pasting those into web tools means handing credentials to a third party. DataEngUtils provides the same utilities in your browser, running client-side with no uploads. This page groups the tools most relevant to AI Engineers into the workflows they hit daily.
Tools for ai engineers
- JWT DebuggerDecode and inspect JSON Web Tokens in your browser without pasting bearer tokens into a public JWT d
- JSON Format / ValidateJSON Format / Validate is built for data engineers who need to validate and format pipeline configs,
- JSON Schema ValidatorJSON Schema Validator is built for data engineers who need to validate and format pipeline configs,
- YAML ↔ JSONYAML ↔ JSON is built for data engineers who need to convert between file formats and schema represen
- JSONL InspectorInspect JSONL (NDJSON) files line by line without uploading them to a converter. DataEngUtils valida
- Regex TesterRegex Tester is built for data engineers who need to inspect, debug, and explain data artifacts when
- Base64 Encode / DecodeBase64 Encode / Decode is built for data engineers who need to encode and decode payloads for APIs,
- URL Encode / DecodeURL Encode / Decode is built for data engineers who need to encode and decode payloads for APIs, log
- Data Type InspectorData Type Inspector is built for data engineers who need to inspect, debug, and explain data artifac
- Hash GeneratorHash Generator is built for data engineers who need to generate identifiers, test rows, and mock pay
- UUID GeneratorUUID Generator is built for data engineers who need to generate identifiers, test rows, and mock pay
- Text Diff CheckerText Diff Checker is built for data engineers who need to inspect, debug, and explain data artifacts
Workflow: Debug authentication tokens
Decode JWTs from your auth flow and inspect the claims without sending the token to a remote debugger.
1. Paste the token
Open JWT Debugger and paste the token. Header, payload and expiry are decoded locally.
2. Check the claims
Review issuer, subject, expiry and custom claims in a syntax-highlighted view.
3. Inspect opaque values
Use Data Type Inspector to identify whether a value is a JWT, UUID, base64 or hash.
Workflow: Validate structured model output
Format JSON responses, validate them against a schema and test the patterns you use to extract fields.
1. Format the response
Paste a raw model response into JSON Format / Validate for pretty-printing and error reporting.
2. Validate the schema
Use JSON Schema Validator to confirm the object matches your expected output contract.
3. Test extraction
Use Regex Tester to refine the patterns that pull fields out of the response.
Workflow: Encode, decode and diff payloads
Base64- and URL-encode request data, check JSONL streams, and diff two responses when a call changes behaviour.
1. Encode a payload
Use Base64 Encode / Decode for binary or text payloads, and URL Encode / Decode for query strings.
2. Check a stream
Use JSONL Inspector to validate JSONL/NDJSON logs line by line.
3. Diff two responses
Use Text Diff Checker to see exactly what changed between two API responses.