LLM-Powered Proposal Generator

• LLM-powered proposal generators are systems that automatically create structured proposals by integrating semantic template extraction, multi-agent orchestration, and retrieval-augmented generation.
• They use advanced retrieval methods and modular pipelines to identify and fill specific document slots, ensuring domain-specific compliance in areas like grants and procurement.
• Multi-agent prompting facilitates interactive fact collection and real-time text generation, reducing errors and enhancing overall proposal quality.

An LLM-Powered Proposal Generator is an automated system that leverages LLMs, retrieval-augmented generation, and structured multi-agent orchestration to produce professional proposals or analogous semi-structured documents. These systems are designed to capture the nuanced requirements of highly regulated, domain-specific workflows—including but not limited to public administration, procurement, grants, and architectural design—by dynamically blending templated sectioning, user-in-the-loop data collection, and various forms of context-aware generation. Their defining innovation is the integration of semantic template extraction and multi-agent prompting, departing from static templates and single-shot LLM prompting, to support both automation and rigorous compliance.

1. System Architectures and Core Modules

LLM-powered proposal generators universally comprise modular pipelines. Central architectural components, as exemplified by Musumeci et al. (Musumeci et al., 2024), are as follows:

Module	Role	LLM Use
Template Retriever	Finds one or more semantic templates by similarity	No/Optional
Semantics-Identification Agent	Extracts per-section instructions and dynamic slots	Yes
Information-Retrieval Agent	Determines missing factual inputs for completion	Yes
Text-Generation Agent	Realizes section text in compliant tone/style	Yes
Document Assembler/Validator	Compiles, checks, outputs final document	Optional (LLM)

The dominant pattern is a loop iterating over sections derived from a retrieved and semantically matched template. For each section, the system identifies the generative intent and required factual “slots,” queries or elicits missing data as needed, composes the text, and validates the resulting segment before aggregation and export.

2. Semantic Template Extraction

Extraction of semantic templates is pivotal for coherent sectioning and for establishing required data. The process, as implemented in (Musumeci et al., 2024), involves:

1. Document Analysis: Parsing a small corpus (e.g., DOCX/PDF proposals) with a block-level extractor (e.g., Adobe PDF-Extract). Each document is split into high-level sections using structural cues.
2. Embedding Computation: Section headings (plus first sentences) are embedded using a transformer-based embedder (examples: OpenAI’s or Sentence-Transformers).
3. Semantic Retrieval: When a user submits a request $q$, its embedding $E_q$ is compared by cosine similarity to each template index $E_i$:

$$\text{sim}(E_q, E_i) = \frac{E_q \cdot E_i}{\|E_q\| \|E_i\|}$$

1. Template Selection: The top-$k$ most similar templates (or a weighted aggregate per

$$P(\text{template}|q) \propto \exp(\alpha\,\text{sim}(E_q,E_i))$$

) are chosen as the semantic “blueprint.”

The upshot is a derived section outline and slot inventory (e.g., Title, Executive Summary, Objectives, Budget, Timeline), forming the structural basis for downstream agents.

3. Multi-Agent Prompting and Fact Collection

Distinct LLM-powered agents execute specialized roles:

• Semantics-Identification Agent: For each section, produces an imperative instruction (e.g., “Summarize the methodology”) and enumerates replaceable slots (e.g., METHODS, SAMPLE_SIZE). Prompts are built from a “System Prompt” describing the agent’s role, plus the section text.

Example (lifted, (Musumeci et al., 2024)):

System: You are a template-analysis assistant. User: Section heading: "Objectives and Outcomes" → Assistant: Instruction: Write a concise list of project Objectives, each followed by the expected Outcome. Slots: [OBJECTIVES], [OUTCOMES]

• Information-Retrieval Agent: Consumes all facts gathered so far and the per-section slot list. If all slots are filled, returns the token [ALL_INFO]. Otherwise, returns a minimal list of missing slots for user input.
• Text-Generation Agent: Given the completed prompt bank and section instructions, produces the required narrative text in accordance with required tone and structural constraints.

A plausible implication is that these agent roles enforce a robust separation of structure, fact management, and stylistic realization—minimizing context-mixing and reducing hallucinated output.

4. Retrieval-Augmented Generation and Integration

The retrieval-augmented paradigm (RAG), emphasized in procurement and design domains (Zhao et al., 2024, Chen et al., 2024), extends this architecture with dense and sparse retrievers, template re-ranking, and explicit, field-level smart tag filling. Notable mechanisms:

• Dense/Sparse Dual Indexing: Embedding-based retrieval (e.g., Faiss+Contriever) captures semantic recall, while an inverted index supports literal/vocabulary matching with term weighting:

$$d\_\text{score}_e(d, f_j) = \cos(E(f_j), E(r_j)), \quad d\_\text{score}_v(d, f_j) = ...$$

Combined field and document ranking follows:

$$d\_\text{score}(d) = \sum_j \text{Avg}(d\_\text{score}_e(d, f_j) + d\_\text{score}_v(d, f_j))$$

• Domain-Conditioned Prompts: Policy clauses and stakeholder requirements are appended to ensure legal or strategic compliance (e.g., “Ensure compliance with Procurement Law Art.22”).
• Smart Tagging: DOCX/Word templates employ inline “smart tags” marking replaceable content, supporting both paragraph and table-level structured filling.
• Post-Generation Validation: Optionally, a validator LLM or rule system checks slot coverage and logical/numeric consistency (e.g., budget summing).

This suggests that integration of retrieval modules not only grounds LLM outputs in realistic prior examples, but also allows for fine-grained control over content accuracy and section completeness.

5. Domain Adaptation: Proposals, Tenders, and Design

The framework is extensible across domains by adjusting the retrieval corpus, template schemas, slot definitions, and compliance requirements:

• Research/Grant Proposals (Musumeci et al., 2024): Templates include sections such as Executive Summary, Objectives, Budget Justification, and Deliverables. Slots correspond to standard proposal components.
• Procurement/Tender Documents (Zhao et al., 2024): Fields include project_name, purchase_item_list (table), legal_clauses, submission_deadline. Retrieval and ranking incorporate field-aware scoring and purchase-item list similarity.
• Architectural and Environmental Design (Chen et al., 2024): Multi-modal agents perform a debate (innovation vs. retrieval), multi-modal fusion (text+image embeddings), and visual rendering (StableDiffusion + ControlNet guided by VLM outputs).

This demonstrates the approach’s adaptability provided a suitable template corpus and slot schema are available.

6. Evaluation Metrics and Empirical Findings

Evaluation in this context includes both qualitative and (optionally) quantitative criteria:

• Slot-Filling Accuracy: $F_1$ score between generated and gold slot values.
• User Intervention Count: Number of times user clarification is sought per section.
• Compliance Rate: Portion of sections adhering to semantic instructions, as assessed by LLM validator or human scoring.
• Quality Score: Human rater-grades for clarity, style, coherence (Likert scale 1–5).
• Efficiency: Wall-clock time or API-call counts compared to single-shot baselines.
• Paragraph/Table Similarity (Zhao et al., 2024):
  • Cosine-based embedding similarity between generated and reference (gold) paragraphs or table cells.

Notable results:

• In medical-procurement use-cases (Zhao et al., 2024), a retrieval+knowledge-augmented system achieved 77.74% overall similarity versus 12.55% for LLM-only baselines.
• In architectural design (Chen et al., 2024), multi-agent, multi-modal systems score higher on both creativity and groundedness compared to DALL·E 3 and vanilla SD v1.5.
• Ablation studies indicate removing retrieval or template filling reduces overall document alignment and completeness.

7. Limitations and Future Directions

Current limitations include:

• Qualitative dependence on human evaluation or LLM “proxy” raters for compliance and style.
• Template corpus coverage constraints: semantic template diversity directly affects generalization to novel requests.
• Integration overhead when scaling to multi-modal or multi-jurisdictional domains; for example, LLM4DESIGN's current dataset is limited to specific urban renewal examples (Chen et al., 2024).
• Real-time interactive editing is not universally supported.

Promising future work includes:

• Expansion of domain coverage and multilingual templates.
• Incorporation of ontology-driven or regulation-mining agents for automatic slot detection.
• Closing the human-in-the-loop gap with interactive and adaptive editing agents.
• Scaling RAG databases with crowd-sourced or post-occupancy feedback.

In conclusion, LLM-powered proposal generators—anchored by semantic template retrieval, multi-agent orchestration, and retrieval-augmented generation—provide a robust and extensible methodology for producing semi-structured, high-compliance documents across diverse highly regulated domains.