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Pengoptimal zero-shot memungkinkan Anda menyempurnakan dan meningkatkan kualitas perintah yang ditulis pengguna secara otomatis. Sering kali, perintah mungkin tidak menghasilkan respons model yang Anda inginkan karena bahasa yang ambigu, kurangnya konteks, atau penyertaan informasi yang tidak relevan. Pengoptimal ini menganalisis dan menulis ulang perintah yang ada agar lebih jelas, lebih efektif, dan lebih selaras dengan kemampuan model, sehingga menghasilkan respons yang berkualitas lebih tinggi.
Pengoptimal zero-shot sangat berguna untuk:
Menyesuaikan dengan Update Model: Saat Anda mengupgrade ke versi model yang lebih baru, perintah yang ada mungkin tidak lagi berfungsi secara optimal.
Meningkatkan Pemahaman Perintah: Jika frasa perintah rumit
atau dapat disalahartikan, alat ini dapat memfrasanya ulang agar lebih jelas dan
akurat, sehingga mengurangi kemungkinan hasil yang tidak diinginkan.
Ada dua cara untuk menggunakan pengoptimal:
Pembuatan Petunjuk: Alih-alih menulis petunjuk sistem yang kompleks
dari awal, Anda dapat menjelaskan sasaran atau tugas Anda dalam bahasa yang sederhana. Pengoptimal kemudian akan membuat serangkaian petunjuk sistem yang lengkap dan terstruktur dengan baik yang dirancang untuk mencapai tujuan Anda.
Penyempurnaan Perintah: Anda memiliki perintah yang berfungsi, tetapi output model tidak konsisten, sedikit di luar topik, atau tidak memiliki detail yang Anda inginkan. Pengoptimal dapat membantu meningkatkan kualitas perintah untuk menghasilkan output yang lebih baik.
Pengoptimal mendukung pengoptimalan perintah dalam semua bahasa yang didukung oleh Gemini dan tersedia melalui Vertex AI SDK
# Import librariesimportvertexaiimportlogging# Google Colab authenticationfromgoogle.colabimportauthPROJECT_NAME="PROJECT"auth.authenticate_user(project_id=PROJECT_NAME)# Initialize the Vertex AI clientclient=vertexai.Client(project=PROJECT_NAME,location='us-central1')# Input original prompt to optimizeprompt="""You are a professional chef. Your goal is teaching how to cook healthy cooking recipes to your apprentice.Given a question from your apprentice and some context, provide the correct answer to the question.Use the context to return a single and correct answer with some explanation."""# Optimize promptoutput=client.prompt_optimizer.optimize_prompt(prompt=prompt)# View optimized promptprint(output.model_dump_json(indent=2))
Objek output ini berjenis OptimizeResponse dan memberikan informasi
tentang proses pengoptimalan. Bagian terpenting adalah
suggested_prompt yang berisi perintah yang dioptimalkan yang dapat Anda gunakan untuk mendapatkan
hasil yang lebih baik dari model Anda. Kolom lainnya, terutama
applicable_guidelines, berguna untuk memahami alasan dan cara perintah Anda ditingkatkan, yang dapat membantu Anda menulis perintah yang lebih baik di masa mendatang. Berikut
contoh outputnya:
{"optimization_mode":"zero_shot","applicable_guidelines":[{"applicable_guideline":"Structure","suggested_improvement":"Add role definition.","text_before_change":"...","text_after_change":"Role: You are an AI assistant...\n\nTask Context:\n..."},{"applicable_guideline":"RedundancyInstructions","suggested_improvement":"Remove redundant explanation.","text_before_change":"...","text_after_change":""}],"original_prompt":"...","suggested_prompt":"Role: You are an AI assistant...\n\nTask Context:\n..."}
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[],[],null,["# Zero-shot optimizer\n\nThe **zero-shot optimizer** lets you automatically refine and improve\nuser-written prompts. Often, a prompt may not produce the model response you\nwant due to ambiguous language, missing context, or the inclusion of irrelevant\ninformation. This optimizer analyzes and rewrites an existing prompt to be\nclearer, more effective, and better aligned with the model's capabilities,\nultimately leading to higher-quality responses.\n\nThe zero-shot optimizer is particularly useful for:\n\n- **Adapting to Model Updates:** When you upgrade to a newer version of a\n model, your existing prompts might no longer perform optimally.\n\n- **Enhancing Prompt Comprehension:** When the phrasing of a prompt is complex\n or could be misinterpreted, the tool can rephrase it for maximum clarity and\n precision, reducing the chance of an undesirable outcome.\n\nThere are two ways to use the optimizer:\n\n- **Instruction Generation**: Instead of writing complex system instructions\n from scratch, you can describe your goal or task in plain language. The\n optimizer will then generate a complete and well-structured set of system\n instructions designed to achieve your objective.\n\n- **Prompt Refinement**: You have a working prompt, but the model's output is\n inconsistent, slightly off-topic, or lacks the detail you want. The\n optimizer can help improve the prompt for a better output.\n\nThe optimizer supports prompt optimization in all languages supported by\nGemini and is available through the [Vertex AI\nSDK](/vertex-ai/generative-ai/docs/reference/libraries)\n\nBefore you begin\n----------------\n\n\nTo ensure that the [Compute Engine default service account](/iam/docs/service-account-types#default) has the necessary\npermissions to optimize prompts,\n\nask your administrator to grant the [Compute Engine default service account](/iam/docs/service-account-types#default) the\nfollowing IAM roles on the project:\n\n| **Important:** You must grant these roles to the [Compute Engine default service account](/iam/docs/service-account-types#default), *not* to your user account. Failure to grant the roles to the correct principal might result in permission errors.\n\n- [Vertex AI User](/iam/docs/roles-permissions/aiplatform#aiplatform.user) (`roles/aiplatform.user`)\n- [Vertex AI Service Agent](/iam/docs/roles-permissions/aiplatform#aiplatform.serviceAgent) (`roles/aiplatform.serviceAgent`)\n\n\nFor more information about granting roles, see [Manage access to projects, folders, and organizations](/iam/docs/granting-changing-revoking-access).\n\n\nYour administrator might also be able to give the [Compute Engine default service account](/iam/docs/service-account-types#default)\nthe required permissions through [custom\nroles](/iam/docs/creating-custom-roles) or other [predefined\nroles](/iam/docs/roles-overview#predefined).\n\nOptimize a prompt\n-----------------\n\n # Import libraries\n import https://cloud.google.com/python/docs/reference/vertexai/latest/\n import logging\n\n # Google Colab authentication\n from google.colab import auth\n PROJECT_NAME = \"PROJECT\"\n auth.authenticate_user(project_id=PROJECT_NAME)\n\n # Initialize the Vertex AI client\n client = https://cloud.google.com/python/docs/reference/vertexai/latest/.Client(project=PROJECT_NAME, location='us-central1')\n\n # Input original prompt to optimize\n prompt = \"\"\"You are a professional chef. Your goal is teaching how to cook healthy cooking recipes to your apprentice.\n\n Given a question from your apprentice and some context, provide the correct answer to the question.\n Use the context to return a single and correct answer with some explanation.\n \"\"\"\n\n # Optimize prompt\n output = client.prompt_optimizer.optimize_prompt(prompt=prompt)\n\n # View optimized prompt\n print(output.model_dump_json(indent=2))\n\nThis `output` object is of type `OptimizeResponse` and provides information\nabout the optimization process. The most important part is the\n`suggested_prompt` which contains the optimized prompt that you can use to get\nbetter results from your model. The other fields, especially\n`applicable_guidelines`, are useful for understanding why and how your prompt\nwas improved, which can help you write better prompts in the future. Here's an\nexample of the output: \n\n {\n \"optimization_mode\": \"zero_shot\",\n \"applicable_guidelines\": [\n {\n \"applicable_guideline\": \"Structure\",\n \"suggested_improvement\": \"Add role definition.\",\n \"text_before_change\": \"...\",\n \"text_after_change\": \"Role: You are an AI assistant...\\n\\nTask Context:\\n...\"\n },\n {\n \"applicable_guideline\": \"RedundancyInstructions\",\n \"suggested_improvement\": \"Remove redundant explanation.\",\n \"text_before_change\": \"...\",\n \"text_after_change\": \"\"\n }\n ],\n \"original_prompt\": \"...\",\n \"suggested_prompt\": \"Role: You are an AI assistant...\\n\\nTask Context:\\n...\"\n }"]]
