Akhir dari "Prompt Besar"
Pada tahap awal pengembangan LLM, pengguna sering mencoba "memaksa" setiap instruksi, batasan, dan titik data ke dalam satu prompt besar. Meskipun terasa intuitif, pendekatan ini menyebabkan overfitting, biaya token tinggi, dan menciptakan "kotak hitam" di mana debugging kegagalan menjadi hampir mustahil.
Industri sedang beralih ke Rantai Prompt. Pendekatan modular ini memperlakukan LLM sebagai serangkaian pekerja khusus, bukan satu-satunya pekerja umum yang terlalu dipaksa bekerja.
Mengapa Rantai Prompt?
- Keandalan:Mendekomposisi tugas kompleks menjadi tugas bawah yang dapat dikelola secara drastis mengurangi tingkat halusinasi.
- Integrasi:Ini memungkinkan Anda secara dinamis menyuntikkan data dari alat eksternal (seperti basis data JSON internal atau API) di tengah alur kerja.
- Efisiensi Biaya:Anda hanya mengirim konteks yang diperlukan untuk setiap langkah tertentu, menghemat token.
Aturan Umum: Dekomposisi Tugas
Satu prompt harus menangani satu pekerjaan spesifik. Jika Anda menemukan diri Anda menggunakan lebih dari tiga pernyataan "dan kemudian" dalam satu instruksi prompt, saatnya untuk merantai mereka menjadi panggilan terpisah.
TERMINALbash — 80x24
> Ready. Click "Run" to execute pipeline.
>
Knowledge Check
Why is "Dynamic Context Loading" (fetching data mid-workflow) preferred over putting all possible information into a single system prompt?
Challenge: Designing a Safe Support Bot
Apply prompt chaining principles to a real-world scenario.
You are building a tech support bot. A user asks for the manual of a "X-2000 Laptop."
Your task is to define the logical sequence of prompts needed to verify the product exists in your database and ensure the final output doesn't contain prohibited safety violations.
Your task is to define the logical sequence of prompts needed to verify the product exists in your database and ensure the final output doesn't contain prohibited safety violations.
Step 1
What should the first two actions in your pipeline be immediately after receiving the user's message?
Solution:
1. Input Moderation: Check if the prompt contains malicious injection attempts. Evaluate as $ (N/Y) $.
2. Entity Extraction: Use a specialized prompt to extract the product name ("X-2000 Laptop") from the raw text.
1. Input Moderation: Check if the prompt contains malicious injection attempts. Evaluate as $ (N/Y) $.
2. Entity Extraction: Use a specialized prompt to extract the product name ("X-2000 Laptop") from the raw text.
Step 2
Once the entity is extracted, how do you generate the final safe response?
Solution:
1. Database Lookup: Query the internal DB for "X-2000 Laptop" manual data.
2. Response Generation: Pass the user query AND the retrieved DB data to the LLM to draft an answer.
3. Output Moderation: Run a final check on the generated text to ensure no safety policies were violated before sending it to the user.
1. Database Lookup: Query the internal DB for "X-2000 Laptop" manual data.
2. Response Generation: Pass the user query AND the retrieved DB data to the LLM to draft an answer.
3. Output Moderation: Run a final check on the generated text to ensure no safety policies were violated before sending it to the user.