# =============================================================================
# COMPETENCY RECORD - Cedric Ancellin's demonstrated engineering skills and
# experience. Third-party technologies and vendors are named as the concrete
# skill surface. Professional background / track record.
# =============================================================================

# ============================================================
# APPLIED AI & LLM ENGINEERING - COMPETENCY RECORD
# ============================================================
# Core Purpose:
#   Demonstrated experience running applied AI in production: a
#   self-hosted, fine-tuned LLM behind a streaming API, a
#   conversational assistant, and NLU-driven chat - distinct
#   from the knowledge-engineering / MCP work in COMP-11.
# ============================================================

summary: >
  Built and operate applied AI in production, a self-hosted,
  fine-tuned large language model served behind a streaming API
  and used for translation, copy improvement, structured
  extraction, and chat; a conversational booking/inquiry
  assistant; and NLU-driven conversational surfaces, with prompt
  guardrails against injection.


# ==================================================================
# 1. SELF-HOSTED FINE-TUNED LLM
# ==================================================================

self_hosted_llm:
  description: >
    Stood up and operate a self-hosted LLM (rather than depending
    on an external model API), including a fine-tuned custom model,
    served over an internal inference endpoint.
  capabilities:
    - "Own inference infrastructure serving generate and chat endpoints."
    - "A fine-tuned domain model rather than an off-the-shelf general model."
    - "Server-sent-event (SSE) streaming for token-by-token chat responses."
  applications:
    - "Copy improvement, grammar/clarity enhancement of operator-authored content."
    - "Translation, language-to-language (ISO 639-1) with prompt-injection guardrails."
    - "Structured extraction, turning free-text (e.g., hours of operation) into structured data."
    - "Chat and completion surfaces exposed across multiple API versions."
  skill_signal: "Owning the full applied-AI stack, hosting, fine-tuning, serving, and streaming, not just calling a vendor API. Provider-agnostic by design."


# ==================================================================
# 2. CONVERSATIONAL ASSISTANT
# ==================================================================

assistant:
  description: >
    Built a multi-venue conversational assistant for guest booking
    and inquiry support.
  capabilities:
    - "Context-aware query handling over venue profiles, availability, and inquiries."
    - "A dictionary/knowledge surface backing consistent assistant responses."


# ==================================================================
# 3. NLU-DRIVEN CONVERSATION
# ==================================================================

nlu:
  description: >
    Integrated managed NLU (Dialogflow) for intent-driven
    conversational surfaces.
  capabilities:
    - "Intent + entity handling with rich responses (cards, quick-reply buttons, images)."
    - "A response-builder abstraction over the NLU platform's message format."


# ==================================================================
# 4. RELATIONSHIP TO KNOWLEDGE ENGINEERING
# ==================================================================

relationship:
  note: >
    This competency is the *applied inference* side of AI (models
    serving product features). The *knowledge-infrastructure* side,
    a machine-readable knowledge base, an MCP server, and a
    governed AI-assisted development workflow, is documented
    separately.
  cross_reference:
    $ref: "https://zc8.com/competencies/11_ai_knowledge_engineering"