Transforming Alteon Support with AI-Powered Support-File Analysis

Introduction

In today's complex networking environments, troubleshooting and resolving issues quickly is critical to maintaining service availability. For Alteon Application Delivery Controllers, the Technical Support File (TSDMP) is a comprehensive log file that plays a pivotal role in identifying and detecting issues. However, analyzing these files presents unique challenges even for experienced engineers.

This article explores some of the key challenges and design decisions that went behind building an enterprise-grade Retrieval-Augmented Generation (RAG) (Zhao et al. 2024) solution to support Alteon TSDMP analysis and transform a resource intensive and expertise-heavy analysis process into a fully automated, 10-minute workflow that delivers expert-level insights.

About TSDMP Files

Alteon TSDMP files contain a wealth of device and network traffic information - system logs, configuration data, statistics, counters, debug logs, system states, and error messages. Over time, with Alteon's extensive market history, these files have naturally evolved, with each successive product version adding diagnostic layers to support new features and emerging troubleshooting requirements.

While this growth in scope and depth of the TSDMP files reflects the product's maturity and deep diagnostic capabilities that support teams rely upon, it also creates some analytical challenges. In its current modern state, TSDMP files can range anywhere from 6,000 to 200,000 lines of technical data. The dense and comprehensive nature of these files, while invaluable for thorough diagnostics, requires expertise to navigate effectively. And even experienced engineers can find extracting relevant insights to be time-intensive. These characteristics, although common to mature enterprise platforms with extensive feature sets, can contribute to longer root cause analysis, impacting Mean Time To Detection (MTTD) and Mean Time To Resolution (MTTR).

Why Traditional RAG Falls Short for TSDMP Analysis

RAG has become the go-to architecture for building AI systems that need to process and analyze domain-specific documents. At its core, RAG combines the power of large language models with external knowledge retrieval, enabling systems to provide accurate, contextual responses based on specific datasets rather than just pre-trained parametric knowledge.

Figure 1: Generic RAG architecture (Zhao et al. 2024 https://arxiv.org/pdf/2402.19473v1)

1. Domain Expertise Gap

However, the challenge of analyzing technical support files from specialized and proprietary networking and cyber-security devices, like the Alteons is unique. These files contain specialized jargon, proprietary software terminology, hardware-specific subsystem logs, and diagnostic patterns that have typically never appeared in any public dataset, and thus they represent a knowledge domain that diverges quite significantly from standard IT/networking troubleshooting data or data from ubiquitous vendors like Cisco, which the LLM might have been exposed to during training. Since an LLM relies on its parametric knowledge to contextualize, interpret, and reason about the retrieved content, language models tend to lack underlying conceptual patterns to interpret domain-specific terminology, relationships, and patterns and perform inadequately when there is a strong divergence between retrieved content and the model’s training distributions

2. Scale & Cost Challenge

TSDMP files vary dramatically in size, largely determined by the Alteon platform that produced them (VX, Standalone, vADC, or VA). In the field, we see anything from ≈ 6,000 to ≈ 200,000 lines per file, with a median around 140,000 lines. This amounts to a range of ~140K to ~1.3M input tokens without even considering additional prompt tokens for optimal in-context learning. Adopting best in class prompting techniques to elicit reasoning traces in LLM responses, increases the number of output tokens to roughly the number of input tokens that were consumed.

Using as an example the current Claude 3.5 Sonnet rates, this footprint translates to ≈ US $0.90 for the smallest files, up to ≈ US $30 for the largest, with the median landing near US $15.

In short, simply streaming raw TSDMP files through an LLM is both expensive and operationally unpredictable—a serious concern when these files are artefacts that support engineers rely on daily to diagnose and troubleshoot customer’s issues. If the support queue were to run dozens of these files every day, the spend would escalate quickly and make it economically impractical for regular use in a support environment—a problem we need to solve.

3. Heterogeneous Composition: Why a “Just‑Pass‑the‑File” Strategy Fails

A TSDMP file is not a single, homogeneous document. Its contents are heterogeneous, roughly amounting to 200 vastly different technical sections. To an LLM, each section requires its own special guidance to steer to the model towards the appropriate next token probability distribution and effective in-context learning (Brown et al. 2020).

Because of this heterogeneity, the classic RAG tactic of “attach the whole file to the prompt and ask the model to diagnose” breaks down:

• No single instruction works for all sections. For example, the instructional heuristics that help an LLM reason about health check utilization debug prints are useless when it encounters Acceleration Engine debug dumps.
• Context-window limits are quickly exceeded. A number of state-of-the-art models top out at 200 k tokens (such as Anthropic Claude Sonnet models), while a large TSDMP bundle can easily surpass that limit on its own.
• Signal-to-noise ratio collapses. Mixing unrelated sections dilutes the statistical cues the model needs to make domain-specific insights, leading to vague or incorrect answers.

In the next section, we will delve into the implementation insights and the impact of our AI-powered solution for TSDMP analysis. We will explore the multi-stage preprocessing engine, routing layer, augment and generation schema, and the overall impact and results of our approach. Stay tuned to learn how these components come together to transform the TSDMP analysis process into a highly efficient and automated workflow.

Implementation Insights

Multi-Stage Preprocessing Engine

A crucial part of our system is the hierarchical domain‑aware preprocessing pipeline that transforms raw TSDMP data into enriched, analyzable components, ready to be fed to the LLM. It is a key part of the implementation to aid in overcoming the three aforementioned challenges. The multi-stage preprocessing schema identifies each semantic block of the TSDMP, adds to it relevant metadata, smooths out chaotic context, and makes it ready to be sent to the LLM, one homogeneous slice at a time. This enables targeted analysis where each section gets processed with its domain-specific expertise, while keeping the context window lean, preserving topical coherence, and allowing us to steer the model with precision, section by section of TSDMP. At a high level, our multi-stage preprocessing comprises of the parser, ablation engine and the context enrichment engine described below:

1. Custom TSDMP Parser

As noted earlier, the TSDMP file consists of heterogeneous data. In addition, the data is saved in various formats and tabular structures. This required a purpose-built parser from scratch that understands Alteon TSDMP specific data, file structures, and is also format aware of the different sections and semantic boundaries. Our parser performs intelligent chunking based on those semantic boundaries and saves the partitions (chunks) in structured storage, which can be easily accessed as key value pairs further down the pipeline.

2. Ablation Engine

Removes noise while preserving relevant diagnostic information in the TSDMP. This includes:

1. a) Granular intelligent filtering at the level of partitions to detect and remove parts of a section that may carry distractors and induce chaotic contexts, eliciting LLM poor performance.
2. b) Data within a section or entire sections that can be ignored owing to incomplete data , disabled subsystems/ modules and other heuristics indicating normal use. Also, the removal of redundant data within or across sections of the partitioned TSDMP. All of this keeps with our goal of efficient LLM usage and cost.

3. Context Enrichment

For reliable and enterprise grade RAG solutions, context enrichment is key and makes data significantly more amenable to LLM reasoning and analysis. After semantic partitioning and ablation , we transform each partition of unstructured or semi-structured TSDMP data into a more digestible, model-friendly format by adding metadata, clarifying ambiguous content, normalizing structure, and injecting domain-specific annotations and examples. This not only improves the LLM’s ability to understand the TSDMP data but also primes it with the right framing to guide token prediction, improve relevance, and reduce hallucinations. Our enrichment includes granular per partition decisions on creating section summaries, key metrics extraction and highlighting, annotation of technical terms, and contextual descriptions. For tables, our enrichment engine performs tabular data enhancement including conversion to clean markdown tables, standardizing timestamps and numeric representations, and adding contextual description of table columns, data type and its content.

Routing Layer

Owing to the heterogeneous nature of the TSDMP , there is a variance in complexity and structure across different semantic partitions, and a one-size-fits-all LLM approach would be prohibitively expensive and computationally wasteful. The routing layer was thus designed to balance latency, and cost-efficiency across the diverse segments of the TSDMP file, thereby optimizing cost of LLM usage and reducing processing time. We designed this to operate on a routing strategy that assigns the right level of LLM and prompt engineering to each partitioned section of the TSDMP. By doing so, we consistently maintain sub-$0.35 processing cost per TSDMP, regardless of TSDMP file size, while also reducing total analysis time.

Augment & Generation Schema

Here is a general outline of how we used partition-aware knowledge engineering, focused context construction, and explainable generation techniques to deliver transparent, reliable and high-precision outputs for each segment of the TSDMP file.

1. Knowledge Engineering

1. a) Relationship Mappings: Here we worked with Alteo experts to draw out cross-section dependencies where issues in one area affect others, temporal relationships between events across different sections, relationship structures in configuration and diagnostic data.
2. b) Baseline Establishment: Domain experts maintain mental models of "normal" system behavior. We worked with Alteon experts to codify this with statistical baselines for hundreds of counters/statistics, threshold definitions that trigger investigation, and pattern libraries of known issue signatures.
3. c) Process Codification: Expert diagnostic reasoning follows established patterns that we captured in terms of triage workflows for prioritizing multiple potential issues, investigation sequences that efficiently narrow down root causes, and validation procedures for confirming diagnoses.

2. Guided In-Context Learning Architecture

Thanks to semantic partitioning into homogeneous topical chunks, we could now use in-context learning techniques to add detailed instructions and guides for section specific processing and analysis.

In general, the context pattern that we used is as follows:

[Technical Data Segment] + [Partition-Specific Analysis Guide] → [Focused Generation]

The upshot of how this works:

1. a) Knowledge injection guides: The instructive guides are stored as configuration files.
2. b) Context pairing: That matches TSDMP data partitions with relevant expert guidance.
3. c) Partition awareness: Each TSDMP section is guided with specialized analysis instructions meant only for that section of the TSDMP.

All of this ensures the LLM receives focused, and coherent data segments. It enabled targeted and partition level expertise to steer the model with applicable domain knowledge for different sections. This prevented information overload that would typically degrade generation quality and limited context to manageable, related information

3. Advanced Generation Techniques

Technical support files often present chaotic contexts where relevant information might be embedded deep within or scattered in between irrelevant data or noise. We used a combination of Chain of Thought (CoT) (Wei et al. 2023), Chain of Note (CoN) (Yu et al. 2024) , Thread of thought (ThoT) (Zhou et al. 2023) and reasoning strategies to get maximal model performance and minimize confabulation and hallucinations.

4. Explainable Reasoning Paths

In mission-critical technical domains, the ability to trace, validate, debug and correct AI-driven analytical decisions is paramount. This necessitates the implementation of observability frameworks that provide transparency into the analytical process. We implemented reasoning tracing which not only improves performance but also provides transparent decision processes of the LLM that can be audited and improved quickly. These include traceable logic chains from observation to conclusion, confidence indicators based on knowledge certainty and data quality. This was pivotal in improving our instructions and guides to the model as well as informing the types of enrichments that were needed for different sections of the TSDMP to get the best performance out of the LLMs

Impact and Results

The transformation from a resource-intensive, expertise-dependent process into an AI-powered automated workflow has delivered measurable improvements in support operations. Support engineers can now obtain expert-level insights immediately, enabling faster escalation decisions and more targeted troubleshooting approaches. During early preview with support teams, we observed dramatic efficiency gains that validate this approach:

• At minimum, a 5x increase observed in analysis efficiency on support cases requiring TSDMP analysis.
• Up to 50x reduction in Mean Time To Detection (MTTD) in some cases.
• Consistent sub-10-minute analysis time regardless of TSDMP file size.

References

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35, 24824-24837.

Yu, W., Zhang, H., Pan, X., Ma, K., Wang, H., & Yu, D. (2023). Chain-of-note: Enhancing robustness in retrieval-augmented language models. arXiv preprint arXiv:2311.09210.

Zhou, Y., Geng, X., Shen, T., Tao, C., Long, G., Lou, J. G., & Shen, J. (2023). Thread of thought unraveling chaotic contexts. arXiv preprint arXiv:2311.08734.

Zhao, P., Zhang, H., Yu, Q., Wang, Z., Geng, Y., Fu, F., ... & Cui, B. Retrieval-augmented generation for ai-generated content: A survey. arXiv 2024. arXiv preprint arXiv:2402.19473.