pub fn base_system(domain: &str, pack_name: &str) -> String {
format!(
"You are a domain expert creating a Domain Knowledge Pack for the '{domain}' domain. \
Pack name: '{pack_name}'. \
Produce structured, high-signal content. Be specific, practical, and avoid generic advice."
)
}
pub fn prompt_system_prompt(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Write a concise LLM system prompt for an agent specialized in '{domain}'. \
It should ground the agent in domain terminology, constraints, and decision patterns. \
Output only the system prompt text, no surrounding explanation."
);
(system, user)
}
pub fn prompt_rules(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Write the core operational rules for the '{domain}' domain as a JSON object with key \"rules\", \
a list of objects each having: id (str), title (str), description (str), \
polarity (\"affirmative\" | \"prohibitive\"), stability (\"stable\" | \"volatile\" | \"experimental\"), \
source_ref (str, use \"generated\"). \
Include must-do rules, must-avoid rules, common failure modes, and key invariants. \
Be specific and actionable. Output ONLY valid JSON, no explanation."
);
(system, user)
}
pub fn prompt_ontology(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Create a domain ontology for '{domain}' as a JSON object with key \"entity_types\", \
a list of objects each having: id (str, kebab-case), name (str), description (str), \
attributes (list of str), relationships (list of objects with name, target_type, cardinality). \
Every entity MUST have at least one relationship to another entity in the ontology. \
cardinality must be one of: \"one-to-one\", \"one-to-many\", \"many-to-many\". \
Example relationship: {{\"name\": \"has-rounds\", \"target_type\": \"funding-round\", \"cardinality\": \"one-to-many\"}}. \
Output ONLY valid JSON, no explanation."
);
(system, user)
}
pub fn prompt_glossary(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Create a domain glossary for '{domain}' as a JSON object with key \"terms\", \
a list of objects each having: id (str), term (str), definition (str), \
stability (\"stable\" | \"volatile\" | \"experimental\"), source_ref (str, use \"generated\"). \
Include at least 20 terms covering core concepts. \
Output ONLY valid JSON, no explanation."
);
(system, user)
}
pub fn prompt_constraints(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Create domain constraints for '{domain}' as a JSON object with keys: \
\"edge_cases\" (list: id, title, description), \
\"anti_patterns\" (list: id, title, description), \
\"hard_limits\" (list: id, title, description). \
Output ONLY valid JSON, no explanation."
);
(system, user)
}
pub fn prompt_decision_trees(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Create 2-3 decision trees for common '{domain}' decisions as a JSON object with key \"trees\", \
a list of objects each having: id (str), title (str), description (str), root (recursive node: \
{{\"question\": str, \"branches\": [{{\"condition\": str, \"next\": node}}]}} or {{\"answer\": str}}). \
Output ONLY valid JSON, no explanation."
);
(system, user)
}
pub fn prompt_chunks_raw(domain: &str, pack_name: &str, context_bundle: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Write comprehensive domain knowledge for '{domain}' structured as discrete, self-contained facts, \
rules, procedures, and patterns. Use markdown headers (##, ###) and numbered lists to separate topics. \
Context summary: {context_bundle}. \
Each unit should be independently useful when retrieved by an agent."
);
(system, user)
}
pub fn prompt_eval_set(domain: &str, pack_name: &str, corpus_excerpt: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Create 15 evaluation entries for the '{domain}' domain knowledge pack. \
Each entry is a JSON object on its own line (JSONL format) with keys: \
\"query\" (a realistic question an agent might face), \
\"expected_dimensions\" (list of answer aspects that should be addressed), \
\"critical_must_include\" (list of 2-4 plain English concept words or phrases — \
choose terms that ARE present in the corpus excerpt below), \
\"scoring_rubric\" (one sentence describing what a good answer looks like). \
Output ONLY the JSONL lines, no surrounding text.\n\n\
Corpus excerpt to ground your must_include terms:\n{corpus_excerpt}"
);
(system, user)
}
pub fn prompt_handbook(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Write a practical '{domain}' handbook for the '{pack_name}' Domain Knowledge Pack. \
Include: introduction, key concepts, practical examples with walkthroughs, \
common pitfalls, implementation checklists, and troubleshooting guidance. \
Format as a well-structured markdown document with clear headers."
);
(system, user)
}
pub fn prompt_quickstart(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Write a quick-start guide for '{pack_name}' covering the '{domain}' domain. \
Target a reader who needs to get productive in under 30 minutes. \
Include: prerequisites, first steps, a minimal worked example, and common gotchas. \
Format as markdown."
);
(system, user)
}
pub fn prompt_faq(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Write an FAQ document for the '{domain}' domain covering 10-15 common questions. \
Each question should have a direct, specific answer. \
Format as markdown with ## headers for each Q&A pair."
);
(system, user)
}
pub fn prompt_examples(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"Write 3-5 worked examples for the '{domain}' domain. \
Each example should show a realistic scenario, the decision process, and the outcome. \
Format as markdown with clear problem/solution structure."
);
(system, user)
}
pub fn prompt_manifest_meta(domain: &str, pack_name: &str) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"For the '{domain}' domain knowledge pack '{pack_name}', write concise values for these manifest fields. \
Respond with ONLY a JSON object containing these exact keys: \
\"audience\" (1-2 sentences describing the target user), \
\"intended_use\" (1-2 sentences on how the pack should be used), \
\"known_limitations\" (1-2 sentences on what the pack does not cover or guarantee). \
Output ONLY the JSON object."
);
(system, user)
}
pub fn prompt_eval_answer(
domain: &str,
pack_name: &str,
query: &str,
context: &str,
) -> (String, String) {
let system = if context.is_empty() {
format!(
"You are a knowledgeable assistant in the '{domain}' domain. \
Answer questions accurately based on your general knowledge."
)
} else {
format!(
"You are a knowledgeable assistant in the '{domain}' domain for the '{pack_name}' knowledge pack. \
Use the following domain knowledge to answer questions:\n\n{context}"
)
};
let user = format!("Answer the following question concisely and accurately:\n\n{query}");
(system, user)
}
pub fn prompt_eval_score(
query: &str,
answer: &str,
rubric: &str,
must_include: &[String],
) -> (String, String) {
let system = "You are an objective evaluator scoring how well an answer meets a scoring rubric. \
Respond with ONLY a JSON object with keys: \"score\" (float 0.0-1.0), \"pass\" (bool) and \"reason\" (one sentence).".to_string();
let must_list = must_include
.iter()
.map(|s| format!("- {s}"))
.collect::<Vec<_>>()
.join("\n");
let user = format!(
"Query: {query}\n\n\
Answer: {answer}\n\n\
Scoring rubric: {rubric}\n\n\
The answer MUST include references to ALL of these concepts:\n{must_list}\n\n\
Score the answer from 0.0 (completely fails the rubric) to 1.0 (fully meets the rubric). \
Respond with ONLY JSON: {{\"score\": 0.0-1.0, \"pass\": true/false, \"reason\": \"one sentence\"}}"
);
(system, user)
}
pub fn prompt_fix_chunks(
domain: &str,
pack_name: &str,
failure_summary: &str,
corpus: &str,
) -> (String, String) {
let system = base_system(domain, pack_name);
let user = format!(
"The eval set for the '{domain}' domain pack '{pack_name}' has failures. \
Failure summary:\n{failure_summary}\n\n\
Rewrite and expand the domain knowledge content to address these gaps. \
Use markdown headers (##, ###) and numbered lists to structure the content. \
Existing corpus for context:\n{corpus}\n\n\
Output comprehensive domain knowledge that covers the missing topics. \
Each section should be independently useful when retrieved by an agent."
);
(system, user)
}