Prompt Engineering Workshop Using Gemini Guided Learning: Templates and Sprints

Make your team productive with prompt engineering — fast

Problem: developers and infra teams waste weeks evaluating LLM prompts, integrations, and safety posture across multiple bots. Promise: a repeatable, hands-on workshop using Gemini Guided Learning that trains engineers to design, evaluate, and iterate high-quality prompts in sprint cycles.

Why this matters in 2026

By early 2026, production teams expect models to be predictable, auditable, and cost-effective. Recent platform advances (late 2024–2025) pushed vendors to provide guided learning features, tighter IDE integrations, and evaluation tooling. That means teams must move beyond ad-hoc prompt tinkering to a disciplined, metrics-driven workflow. This workshop plan uses Gemini Guided Learning as the hands-on learning platform and pairs it with evaluation-as-code and iterative sprints so your team ships prompt designs that are robust, secure, and performant.

Workshop overview — outcomes and audience

Outcomes:

• Engineers can craft reproducible prompt templates and few-shot patterns for specific tasks (summarization, code generation, extraction).
• Teams can run objective evaluation metrics and measure hallucination, latency, cost, and task success rate.
• Participants complete a 3-sprint cycle of prompt improvement and deploy a tested prompt as a microservice or CI job.

Target audience: backend engineers, ML engineers, SREs, and developer advocates who already know HTTP APIs, basic Python/JS, and Git.

Logistics and prerequisites

• Duration: half-day workshop (4 hours) or full-day with extended sprints (7–8 hours). Recommended: two sessions — Theory+Demo, then Sprints.
• Attendee ratio: 1 instructor per 8–12 participants for hands-on help.
• Accounts: access to Gemini Guided Learning (enterprise/workspace recommended), GitHub/GitLab, and a small cloud VM for local evaluation runners.
• Materials: starter repo with test sets, evaluation harness, prompt templates, and CI examples (we provide downloadable templates in the CTA).

Workshop agenda — inverted pyramid: start with results

Session 1 — 60–75 minutes: Foundations & Demo

• Hook (5 min): show a failing prompt vs. an improved one and its metrics (latency, accuracy, hallucination rate).
• Guided Learning tour (15–20 min): instructor demo of Gemini Guided Learning: structured lessons, interactive checkpoints, and how to author guided exercises for developers.
• Prompt design principles (20 min): short list of best practices with examples (system vs. user messages, explicit constraints, format specifiers, and anchor examples).
• Live demo (15–20 min): build a prompt template for an API extraction task and run a test batch. Share results and discuss failure modes.

Session 2 — 3 sprints (repeatable) — 2:15–3:45 hours

Each sprint follows the Plan/Do/Measure/Improve (PDMI) loop. Keep sprints short (30–45 minutes) to force focused experiments.

1. Sprint 0 — Plan & Baseline (30–40 min)
   • Define the task and success criteria (example: extract structured fields from support emails with ≥95% field accuracy and <2% hallucination rate).
   • Pick baseline prompt template from starter repo and run it on the baseline dataset (20–30 examples).
   • Record baseline metrics: accuracy, precision/recall, latency, cost per call, and qualitative failure categories.
2. Sprint 1 — Targeted changes (30–45 min)
   • Apply a single change (e.g., add a strict output schema or a few-shot example set) and re-run tests.
   • Measure deltas and log trade-offs (e.g., 2× latency for 10% accuracy gain).
   • Decide whether to keep the change for the next sprint.
3. Sprint 2 — Robustness & Edge Cases (30–45 min)
   • Inject adversarial or noisy inputs from a curated dataset and measure performance under stress.
   • Introduce constraints to reduce hallucinations (e.g., add evidence grounding or chain-of-thought restrictions) — consider hybrid oracle patterns for regulated evidence flows.
   • Finalize the prompt and prepare a deployment checklist (monitoring, cost controls, rate limits).

Prompt templates — reusable patterns

Below are compact templates you can paste into Gemini Guided Learning exercises. Each template uses a clear system instruction and an explicit output schema.

1) Extraction template (JSON schema)

System: You are a precise extractor. Output must be valid JSON and follow the exact keys: customer_name, issue_summary, priority (low|medium|high), timestamp.

User: "<email text>"

Assistant: "Provide valid JSON only. If a field is missing, use null. Timestamp must be ISO8601."

2) Summarization with constraints

System: Summarize concisely. Max 40 words. Preserve technical terms. Use bullet list if multiple action items.

User: "<document>"

Assistant: "Summary:"

3) Code-generation safe template

System: When generating code, include a short explanation and only reliable standard-library imports. Annotate any external API calls with a disclaimer.

User: "Implement a function to parse X and return structured Y. Provide unit tests."

Evaluation metrics — objective & practical

Move beyond subjective QA. Combine automated metrics with human review.

Core quantitative metrics

• Task accuracy: percent of outputs that meet the functional spec.
• Precision / Recall: for extraction tasks.
• BRO (Behavioral Robustness): success under noisy/adversarial inputs (new 2025 trend: standardized robustness suites).
• Hallucination rate: percent of outputs asserting unverifiable facts (requires reference checks).
• Latency and P95/P99: real-time constraints matter for production agents.
• Cost per 1k calls: compute cost trade-offs for higher-context prompts.

Human-in-the-loop metrics

• Time-to-complete: how long an engineer takes to produce a production-quality prompt.
• Reviewer agreement: kappa score among human reviewers for critical fields.
• User satisfaction: endpoint consumers’ rating on quality, relevance, and trust.

Evaluation-as-code example (Python)

from pathlib import Path
import json
from evaluation_harness import run_batch, compute_metrics

baseline = Path('data/baseline.jsonl')
results = run_batch(prompt_template='templates/extractor.json', input_file=baseline)
metrics = compute_metrics(results, reference_file='data/refs.jsonl')
print(json.dumps(metrics, indent=2))

Use CI (GitHub Actions or GitLab CI) to fail builds when core metrics drop below thresholds — integrate this with your repo-level tooling and build gates.

Integrations: from prototype to production

Shipping a prompt means connecting it into your stack and automating evaluation.

Quick integration patterns

• Microservice wrapper: expose a small FastAPI service that applies the prompt template and enforces schema validation — see local appliance guides for privacy-first patterns (local-first sync appliances).
• CI Gate: run evaluation-as-code on PRs to detect regressions in prompt behavior — tie this into your PR gating strategy (evaluation pipelines).
• Observability: log request/response hashes, output schema conformance, latency, and cost tags to your APM/Datadog (observability).
• Permissioning: require encryption-at-rest for logs and redaction of PII before sending to the model — follow zero-trust storage patterns (zero-trust storage).

Example: FastAPI wrapper (Python)

from fastapi import FastAPI, Body
from pydantic import BaseModel
import google.generativeai as genai

app = FastAPI()
genai.configure(api_key='YOUR_API_KEY')

class Input(BaseModel):
    text: str

@app.post('/extract')
async def extract(payload: Input):
    prompt = f"System: You are an extractor...\nUser: {payload.text}"
    response = genai.generate(prompt=prompt, model='gemini-pro')
    return response

Note: in production, use enterprise credentials, private endpoint options, and request redaction.

Security, privacy & compliance checklist

• Use workspace/enterprise keys with fine-grained access controls.
• Review vendor data retention and model training policies before sending PII — align with reader data trust principles.
• Implement prompt redaction and local anonymization pipelines.
• Log hashes instead of raw data, and store only the minimum necessary artifacts.
• For regulated domains, run model outputs through a policy filter and human review step.

Common failure modes and fixes

• Non-compliant format: enforce JSON schemas and reject invalid outputs at the wrapper layer.
• Hallucinations: add evidence constraints, reduce creative temperature, or provide retrieval-augmented context.
• High latency/cost: reduce context window, use distilled models for low-risk tasks, cache deterministic responses.
• Overfitting to examples: diversify few-shot examples and include adversarial tests during sprints.

Facilitation tips for instructors

• Start with a clear metric that everyone agrees on—avoid open-ended “quality” goals.
• Encourage binary experiments: change one variable per sprint to measure causality.
• Use pair programming for first iterations—one writes prompts, the other writes tests.
• Capture every prompt and seed in Git with versioned tags so you can roll back or compare.

Case study (scenario-based example)

In late 2025 an internal engineering team moved a support-ticket triage pipeline from rule-based heuristics to LLM-assisted extraction. They ran a two-day workshop using a similar sprint plan. Baseline accuracy was 78% with 7% hallucination. After three sprints they achieved 94% accuracy and cut triage latency by 60% while keeping cost per transaction within budget. The differences came from: stricter output schemas, retrieval-augmented evidence checks, and CI-driven evaluation that prevented regressions during model/dep updates — a result typical when teams adopt modern observability & evaluation-as-code practices.

Advanced strategies & 2026 predictions

Expect these trends to matter in 2026 and beyond:

• Evaluation-as-code becomes standard: Teams will check prompt regressions with the same rigor as unit tests.
• Automated prompt optimization: tools will propose prompt edits and a/b test them in production sandboxes; your human-driven PDMI loops will remain essential for safety checks.
• Model cards and provenance: standardized model metadata will simplify compliance checks and reduce hallucinations by integrating evidence sources — align this with zero-trust storage and provenance.
• IDE-native guidance: Gemini and other vendors will offer in-IDE prompt linters and performance estimators for developers — pair this with hardening local JS tooling and editor integrations.
• Modular prompt libraries: teams will publish vetted, composable prompt modules (extractors, summarizers, validators) as internal packages.

Workshop deliverables (what participants take away)

• Prompt templates and the Guided Learning lesson pack preloaded into your workspace.
• A reproducible evaluation harness and CI config to run on every PR.
• Deployment checklist covering security, monitoring, and rollback instructions.
• A prioritized backlog for further prompt improvements and integrations (3–6 items).

Wrap-up: Actionable next steps

1. Run the baseline: pick one high-impact task and measure current metrics in 30–60 minutes.
2. Run two sprints: apply the PDMI cycle twice and log improvements.
3. Commit prompts and evaluation tests to version control and gate PRs with metric checks (use repo-level gates).
4. Schedule a follow-up workshop in 2–4 weeks to iterate on real production data.

“Training on prompts without measurable metrics is only opinion — convert your intuition into tests.”

Resources & templates

• Starter repo: prompt templates, datasets, and CI snippets (link provided in CTA).
• Evaluation harness examples for Python/Node and GitHub Actions.
• Guided Learning lesson blueprint to import into Gemini Guided Learning for in-house training.

Final call-to-action

Ready to run this workshop with your team? Download the starter repo, Guided Learning lesson pack, and CI templates from our workshop toolkit. If you want a hands-on session tailored to your use case, book a facilitated workshop with our engineers — we’ll help you define metrics, set up evaluation-as-code, and ship a production-ready prompt microservice in a single week.

Get the toolkit and schedule a workshop: visit the ebot.directory workshop page or clone the repo to start now.

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