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How to Use Baichuan for Model selection checklists
Learn Baichuan model selection checklists with step by step workflows, realistic examples, and verified plan notes.
Baichuan works well for model selection checklists when you run it like production work: locked brief, SOURCE facts, then review before publish. Baichuan provides large language models and related AI services. Confirm live model names, access, and pricing on the official Baichuan website. Do not invent quotas or enterprise terms. Start at /explore/baichuan.
This guide focuses on model selection checklists in detail. Related Baichuan articles: /blog/how-to-use-baichuan-for-safety-filter-configuration, /blog/how-to-use-baichuan-for-token-usage-monitoring, /blog/how-to-use-baichuan-for-enterprise-access-planning.
When this workflow is the right job
Use model selection checklists when the deliverable is specifically this Baichuan job. Switch to chat completion prototypes when that workflow already owns the asset.
Step by step workflow
1. Brief Model selection checklists
Write what must stay true for model selection checklists in Baichuan before settings or spend.
Brief: Model selection checklists Keep: verified SOURCE facts only Avoid: invented pricing or features Success: one reviewable output
2. Open Baichuan for Model selection checklists
Use the Baichuan surface that owns model selection checklists. Do not mix a neighboring workflow in the same pass.
Surface: Model selection checklists Start: pilot with one representative input Plans: www.baichuan-ai.com
3. Pilot Model selection checklists
Run a single model selection checklists pilot. Score clarity, grounding, and whether the output is reviewable.
Pilot: Model selection checklists [ ] SOURCE facts match [ ] Output reviewable [ ] Settings logged
4. Refine Model selection checklists
Change one model selection checklists dimension only. Save a template from the best run.
Refine: Model selection checklists Change: one control only Keep: SOURCE and success criteria
Practical model selection checklists examples
Spend alert
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Spend alert". Objective: Ship a reviewable API/prompt result for Spend alert with spend controls. Inputs: - Prompt and schema for Spend alert - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Spend alert → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Spend alert with sample output and monitoring notes.
Cache safe replies
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Cache safe replies". Objective: Ship a reviewable API/prompt result for Cache safe replies with spend controls. Inputs: - Prompt and schema for Cache safe replies - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Cache safe replies → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Cache safe replies with sample output and monitoring notes.
Safety filter
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Safety filter". Objective: Ship a reviewable API/prompt result for Safety filter with spend controls. Inputs: - Prompt and schema for Safety filter - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Safety filter → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Safety filter with sample output and monitoring notes.
Bilingual draft
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Bilingual draft". Objective: Ship a reviewable API/prompt result for Bilingual draft with spend controls. Inputs: - Prompt and schema for Bilingual draft - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Bilingual draft → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Bilingual draft with sample output and monitoring notes.
System prompt lock
Scenario: An engineer prototypes Model selection checklists via Baichuan for "System prompt lock". Objective: Ship a reviewable API/prompt result for System prompt lock with spend controls. Inputs: - Prompt and schema for System prompt lock - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot System prompt lock → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for System prompt lock with sample output and monitoring notes.
Temp 0.2
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Temp 0.2". Objective: Ship a reviewable API/prompt result for Temp 0.2 with spend controls. Inputs: - Prompt and schema for Temp 0.2 - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Temp 0.2 → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Temp 0.2 with sample output and monitoring notes.
Max tokens note
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Max tokens note". Objective: Ship a reviewable API/prompt result for Max tokens note with spend controls. Inputs: - Prompt and schema for Max tokens note - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Max tokens note → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Max tokens note with sample output and monitoring notes.
Retry backoff
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Retry backoff". Objective: Ship a reviewable API/prompt result for Retry backoff with spend controls. Inputs: - Prompt and schema for Retry backoff - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Retry backoff → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Retry backoff with sample output and monitoring notes.
Eval set
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Eval set". Objective: Ship a reviewable API/prompt result for Eval set with spend controls. Inputs: - Prompt and schema for Eval set - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Eval set → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Eval set with sample output and monitoring notes.
PII scrub
Scenario: An engineer prototypes Model selection checklists via Baichuan for "PII scrub". Objective: Ship a reviewable API/prompt result for PII scrub with spend controls. Inputs: - Prompt and schema for PII scrub - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot PII scrub → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for PII scrub with sample output and monitoring notes.
Latency budget
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Latency budget". Objective: Ship a reviewable API/prompt result for Latency budget with spend controls. Inputs: - Prompt and schema for Latency budget - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Latency budget → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Latency budget with sample output and monitoring notes.
Single-tenant note
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Single-tenant note". Objective: Ship a reviewable API/prompt result for Single-tenant note with spend controls. Inputs: - Prompt and schema for Single-tenant note - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Single-tenant note → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Single-tenant note with sample output and monitoring notes.
Key rotate
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Key rotate". Objective: Ship a reviewable API/prompt result for Key rotate with spend controls. Inputs: - Prompt and schema for Key rotate - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Key rotate → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Key rotate with sample output and monitoring notes.
Log redaction
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Log redaction". Objective: Ship a reviewable API/prompt result for Log redaction with spend controls. Inputs: - Prompt and schema for Log redaction - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Log redaction → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Log redaction with sample output and monitoring notes.
Fallback model
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Fallback model". Objective: Ship a reviewable API/prompt result for Fallback model with spend controls. Inputs: - Prompt and schema for Fallback model - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Fallback model → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Fallback model with sample output and monitoring notes.
Pilot prompt
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Pilot prompt". Objective: Ship a reviewable API/prompt result for Pilot prompt with spend controls. Inputs: - Prompt and schema for Pilot prompt - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Pilot prompt → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Pilot prompt with sample output and monitoring notes.
Chat completion
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Chat completion". Objective: Ship a reviewable API/prompt result for Chat completion with spend controls. Inputs: - Prompt and schema for Chat completion - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Chat completion → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Chat completion with sample output and monitoring notes.
JSON schema out
Scenario: An engineer prototypes Model selection checklists via Baichuan for "JSON schema out". Objective: Ship a reviewable API/prompt result for JSON schema out with spend controls. Inputs: - Prompt and schema for JSON schema out - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot JSON schema out → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for JSON schema out with sample output and monitoring notes.
Summarize endpoint
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Summarize endpoint". Objective: Ship a reviewable API/prompt result for Summarize endpoint with spend controls. Inputs: - Prompt and schema for Summarize endpoint - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Summarize endpoint → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Summarize endpoint with sample output and monitoring notes.
Router model pick
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Router model pick". Objective: Ship a reviewable API/prompt result for Router model pick with spend controls. Inputs: - Prompt and schema for Router model pick - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Router model pick → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Router model pick with sample output and monitoring notes.
Stream tokens
Scenario: An engineer prototypes Model selection checklists via Baichuan for "Stream tokens". Objective: Ship a reviewable API/prompt result for Stream tokens with spend controls. Inputs: - Prompt and schema for Stream tokens - Model/endpoint choice - Token/spend limits - Safety filters Workflow: Create key → Configure Model selection checklists → Pilot Stream tokens → Log usage → Add retries/filters → Productionize Requirements: - Stay within verified Baichuan capabilities; do not invent features. - Confirm live plan notes on www.baichuan-ai.com before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A working Model selection checklists prototype for Stream tokens with sample output and monitoring notes.
How to improve model selection checklists
Cut noise from model selection checklists by removing extra adjectives while preserving SOURCE facts in Baichuan.
Raise quality by insisting on a single success check before debating style.
Make review easier by labeling fields that must never change.
Speed iteration by cloning the last good run and altering only one control.
Stabilize outputs by pinning settings after the pilot is approved.
Reduce rework by rejecting drafts that invent claims.
Improve handoffs by recording which control produced the best result.
Harden the workflow by testing an incomplete input before trusting defaults.
Prompting and usage guidance
Name the model selection checklists job, audience, and success check before opening Baichuan.
Paste only verified facts under SOURCE so Baichuan cannot invent details.
Specify the deliverable shape up front.
Call out fixed details versus flexible style choices.
Ask Baichuan to flag unsupported claims before you accept the draft.
Limitations to respect
Check Baichuan plan gates for model selection checklists on www.baichuan-ai.com before you promise timelines.
Keep drafts unpublished until a human confirms SOURCE facts.
Baichuan can be wrong. Treat model selection checklists as provisional until review.
If documentation is silent on a claim, leave it out rather than guessing.
Practical tips for this workflow
Pilot once before batching model selection checklists in Baichuan.
Keep a reusable template with variables for model selection checklists.
Separate creative instructions from SOURCE facts.
Log settings from the best run.
Common mistakes
- Skipping the pilot run before scaling volume
- Inventing pricing, quotas, or features not on official pages
- Mixing unrelated workflows in one session
- Publishing without a human review gate
Treat model selection checklists in Baichuan as a production workflow: brief, pilot, refine, then ship with review. Related reading: /blog/how-to-use-baichuan-for-safety-filter-configuration, /blog/how-to-use-baichuan-for-token-usage-monitoring, /blog/how-to-use-baichuan-for-enterprise-access-planning.