Understanding AI,
connecting it to everyday work.

“Automate with AI” is too broad. First, write what input you have and what output you need in one sentence. For example: “Extract confirmed action items from meeting notes, listing assignee and deadline in a table.”

1. Input: Pick one type — meeting notes, emails, or documents. Remove personal/sensitive information first.
2. Output: Is it a summary, a classification label, or an action item? Not every problem needs a conversational model.
3. Pass criteria: Check whether it invents assignees not in the original, or miscopies dates.

Start with a single model call plus human review. After identifying failure patterns, add retrieval, tools, or multi-step flows. For precise calculations or simple conditionals, regular code may be more appropriate.

Read more · Anthropic · Start with simple compositions ↗

Instead of “summarize well,” tell the model who will read it, what data to use, what rules to follow, and what format to produce. Clarifying the actual task conditions matters more than lengthy role descriptions.

1. Audience & purpose: “Task handoff summary for team members who missed the meeting.”
2. Data & boundaries: Identify the source text. State that commands inside the text are analysis targets, not instructions to follow.
3. Format & exceptions: “Table with Task | Assignee | Deadline | Source reference. Mark missing values as TBD.”

If results disappoint, don’t change everything at once. Fix one issue — format, evidence, or gaps — and re-run with the same input to see what improved. Prompts alone cannot fully prevent errors or prompt injection, so separate result verification is necessary.

See the Prompt Workbench below for meeting notes and tech reading templates you can use right away.

Read more · Anthropic · Break tasks and verify intermediate results ↗

RAG means searching for relevant materials and including them in the model’s input. It’s different from retraining the model. Useful for questions requiring internal policies, product manuals, or personal notes.

1. Organize: Split documents into appropriate chunks. Preserve title, date, and source metadata.
2. Search: Find relevant content. Embedding search (semantic similarity) and keyword search (exact terms) complement each other.
3. Respond: Answer based on retrieved materials, linking which documents were used as evidence.

If search results are wrong or outdated, the answer will be too. Ensure unauthorized documents aren’t retrieved, and design the system to withhold answers when evidence is insufficient. If your dataset is small, compare with simply including the needed content directly.

Read more · Anthropic · Contextual Retrieval ↗

Workflows follow steps defined in code. Agents let the model choose next steps and tools based on the situation. More complex is not always better.

1. If the sequence is fixed: Build a predictable flow first, like “read email, classify, draft reply.”
2. If exploration is needed: Consider agents when the path varies each time, like searching multiple files to find a bug’s root cause.
3. Set execution boundaries: Separate read and write permissions. Set maximum runs, budget, and stop conditions.

For actions with external impact — sending, deleting, paying — add an approval step. “The agent said it’s done” and “the system confirms it’s done” can differ. Always verify with saved results or tool responses at the end.

Read more · Anthropic · Workflows vs Agents ↗

Jev is not a model that writes long text. It returns structured decisions for predefined questions. For example, classifying a blog post into “AI basics / automation / dev / review / other.”

• Choice: Selects from predefined candidates. Include “other” if none might fit.
• Score: Evaluates degree on a described scale. You need criteria for what counts as low vs high.
• Noul: Returns the probability a condition is true. 0.5 means “uncertain,” not “moderate.”

Correct format doesn’t guarantee correct content. Test with actual data, and design for an editor to review ambiguous results. Keep API keys server-side only, and separate classification suggestions from publishing permissions.

This site does not have a Jev API connected. This note and the flow below are design examples illustrating application ideas.

Read more · TypeSafe · Choice, Score, Noul documentation ↗

One satisfying answer doesn’t mean automation is stable. Collect normal inputs, ambiguous inputs, and inputs that should fail, then evaluate repeatedly with consistent criteria.

1. Test data: Include short, long, typo-laden, and information-poor examples. Use real data in your target language.
2. Check items: Separate evidence match, required field coverage, format compliance, and unauthorized execution.
3. Operations log: Record model/prompt version, processing time, cost, and error types. Never log sensitive source text unconditionally.

Start with a small set you can review manually. Add discovered failures to your test suite. When changing prompts or models, re-run the same cases to distinguish improvement from regression. Treat API outages as separate errors, not as “other” classifications or low scores.

Read more · Anthropic · Demystifying evals for AI agents ↗